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ASME Conference Presenter Attendance Policy and Archival Proceedings

2014;():V002T00A001. doi:10.1115/DSCC2014-NS2.
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This online compilation of papers from the ASME 2014 Dynamic Systems and Control Conference (DSCC2014) represents the archival version of the Conference Proceedings. According to ASME’s conference presenter attendance policy, if a paper is not presented at the Conference, the paper will not be published in the official archival Proceedings, which are registered with the Library of Congress and are submitted for abstracting and indexing. The paper also will not be published in The ASME Digital Collection and may not be cited as a published paper.

Commentary by Dr. Valentin Fuster

Dynamical Modeling and Diagnostics in Biomedical Systems

2014;():V002T16A001. doi:10.1115/DSCC2014-5854.

In this work, we model electroencephalography (EEG) signals as the stochastic output of a double Duffing - van der Pol oscillator networks. We develop a novel optimization scheme to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions and derive models with outputs that show very good agreement with EEG signals in terms of both frequency and information contents. The results, reinforced by statistical analysis, shows that the EEG recordings under EC and EO resting conditions are distinct realizations of the same underlying model occurring due to parameter variations. Furthermore, the EC and EO EEG signals each exhibit distinct nonlinear dynamic characteristics. In summary, it is established that the stochastic coupled nonlinear oscillator network can provide a useful framework for modeling and analysis of EEG signals that are recorded under variety of conditions.

Commentary by Dr. Valentin Fuster
2014;():V002T16A002. doi:10.1115/DSCC2014-5857.

This article compares stochastic estimates of human ankle mechanical impedance when ankle muscles were fully relaxed and co-contracting antagonistically. We employed Anklebot, a rehabilitation robot for the ankle to provide torque perturbations. Surface electromyography (EMG) was used to monitor muscle activation levels and these EMG signals were displayed to subjects who attempted to maintain them constant. Time histories of ankle torques and angles in the lateral/medial (LM) directions were recorded. The results also compared with the ankle impedance in inversion-eversion (IE) and dorsiflexion-plantarflexion (DP). Linear time-invariant transfer functions between the measured torques and angles were estimated for the Anklebot alone and when a human subject wore it; the difference between these functions provided an estimate of ankle mechanical impedance. High coherence was observed over a frequency range up to 30 Hz. The main effect of muscle activation was to increase the magnitude of ankle mechanical impedance in all degrees of freedom of ankle.

Commentary by Dr. Valentin Fuster
2014;():V002T16A003. doi:10.1115/DSCC2014-5870.

In this paper, we investigate the effects of including scientific tasks on the satisfaction of patients performing rehabilitation exercises. A low-cost system, comprised of a haptic joystick and a laptop computer, is used for patients to interact with a virtual environment. Within the virtual environment, users are presented with and classify images captured by a robot as part of a citizen science project. Results show that higher levels of satisfaction are attained when the exercise includes scientific tasks.

Commentary by Dr. Valentin Fuster
2014;():V002T16A004. doi:10.1115/DSCC2014-5912.

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia such as Alzheimer’s disease (AD). This study explores non-event-related multiscale entropy (MSE) measures as features for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years) — 15 normal controls (NC), 16 MCI, and 17 early AD — are examined. Multiscale entropy curves are computed for short EEG segments and averaged over the segments. Binary discriminations among the three groups are conducted using support vector machine models. Leave-one-out cross-validation accuracies of 80.7% (p-value <0.0018) for MCI vs. NC, 87.5% (p-value <1.322E−4) for AD vs. NC, and 90.9% (p-value <2.788E−5) for MCI vs. AD are achieved. Results demonstrate influence of cognitive deficits on multiscale entropy dynamics of non-event-related EEG.

Commentary by Dr. Valentin Fuster
2014;():V002T16A005. doi:10.1115/DSCC2014-5929.

In this study, a stochastic Duffing - van der Pol coupled two oscillator system is designed to produce output matching the information content, complexity measure, and frequency content of actual electroencephalography (EEG) signals. This is achieved by deriving the oscillator model parameters and noise intensity using an optimization scheme whose objective is to minimize a weighed average of errors in sample entropy, Shannon entropy, and powers of the major brain frequency bands. The signals produced by the optimal model are then compared with the EEG signal using phase portrait reconstruction. The study shows that the model can effectively reproduce signals that match EEG recorded under different brain states with respect to multiple metrics.

Commentary by Dr. Valentin Fuster
2014;():V002T16A006. doi:10.1115/DSCC2014-5941.

Repetitive concussions and sub-concussions suffered by athletes have been linked to a series of sequelae ranging from traumatic encephalopathy to dementia pugilistica. We developed a detailed finite element model of the human head based on standard libraries of medical imaging. The model includes realistic material properties of the brain tissue, bone, soft tissue, and cerebral spinal fluid, as well as a helmet. The strains/stresses and pressure gradients and concentrations created in the brain tissue due to propagation of waves produced by the impact through the complex internal structure of the human head for various impact scenarios were studied. This approach has the potential to expand our understanding of the mechanism of brain injury, and to better assessment of risk of delayed neurological disorders for tens of thousands of young athletes throughout the world.

Commentary by Dr. Valentin Fuster
2014;():V002T16A007. doi:10.1115/DSCC2014-5955.

Cerebral palsy is caused by an injury to the brain, but also causes many secondary changes in the musculoskeletal system. Altered muscle properties such as contracture, an increased passive resistance to stretch, are common but vary widely between individuals and between muscles. Quantifying these changes is important to understand pathologic movement and create patient-specific treatment plans. Musculoskeletal modeling and simulation have increasingly been used to evaluate pathologic movement in CP; however, these models are based upon muscle properties of unimpaired individuals. In this study, we used a dynamic musculoskeletal simulation of a simple motion, passively moving the ankle, to determine (1) if a model based upon unimpaired muscle properties can accurately represent individuals with cerebral palsy, and (2) if an optimization can be used to adjust passive muscle properties and characterize magnitude of contracture in individual muscles. We created musculoskeletal simulations of ankle motion for nine children with cerebral palsy. Results indicate that the unimpaired musculoskeletal model cannot accurately characterize passive ankle motion for most subjects, but adjusting tendon slack lengths can reduce error and help identify the magnitude of contracture for different muscles.

Commentary by Dr. Valentin Fuster
2014;():V002T16A008. doi:10.1115/DSCC2014-5993.

For recognizing human motion intent, electromyogram (EMG) based pattern recognition approaches have been studied for many years. A number of methods for classifying EMG patterns have been introduced in the literature. On the purpose of selecting the best performing method for the practical application, this paper compares EMG pattern recognition methods in terms of motion type, feature extraction, dimension reduction, and classification algorithm. Also, for more usability of this research, hand and finger EMG motion data set which had been published online was used. Time-domain, empirical mode decomposition, discrete wavelet transform, and wavelet packet transform were adopted as the feature extraction. Three cases, such as no dimension reduction, principal component analysis (PCA), and linear discriminant analysis (LDA), were compared. Six classification algorithms were also compared: naïve Bayes, k-nearest neighbor, quadratic discriminant analysis, support vector machine, multi-layer perceptron, and extreme machine learning. The performance of each case was estimated by three perspectives: classification accuracy, train time, and test (prediction) time. From the experimental results, the time-domain feature set and LDA were required for the highest classification accuracy. Fast train time and test time are dependent on the classification methods.

Commentary by Dr. Valentin Fuster
2014;():V002T16A009. doi:10.1115/DSCC2014-6017.

Conventional prosthetic devices substitute lost human limbs with mechanical proxies to enable amputees perform daily chores. We present an alternative approach that may replace or supplement traditional upper-limb prostheses by utilizing and enhancing the functionality of the remaining healthy limb with a new type of wrist-mounted robot: the Supernumerary Robotic (SR) Fingers. These SR Fingers are naturally and implicitly coordinated with the motion of the human fingers to provide assistance in a variety of prehensile tasks that are usually too difficult to carry out with a single hand, such as grasping a large/oddly shaped object or taking the lid off a jar. A novel control algorithm, termed “Bio-Artificial Synergies”, is developed so the SR Fingers can share a work load and adapt to diverse task conditions just like the real fingers do. Through grasp experiments and data analysis, postural synergies were found for a seven-fingered hand comprised of two SR Fingers and five human fingers. The synergy-based control law was then extracted from the experimental data using Partial Least Squares Regression (PLSR) and tested on the SR Finger prototype on a number of common tasks to demonstrate the usefulness and effectiveness of this new class of prosthetic device.

Topics: Prostheses , Robotics
Commentary by Dr. Valentin Fuster
2014;():V002T16A010. doi:10.1115/DSCC2014-6200.

This paper presents a novel method for creating an electrical stimulation pattern to control the equilibrium-point (EP) of human ankle movement. Focusing on the synergetic activation of agonist–antagonist (AA) muscles, the proposed method employs the ES-AA ratio (the ratio of the electrical stimulation levels for AA muscles) and the ES-AA sum (the sum of the electrical stimulation levels for AA muscles), which are based on the AA ratio (the ratio of the electromyography (EMG) voltage levels for AA muscles) and the AA sum (the sum of the EMG voltage levels for AA muscles) used in human movement analysis [1, 2]. The ES-AA ratio is related to the EP of the joint whereas the ES-AA sum is associated with mechanical stiffness of the joint. Using the AA concepts, we estimated the transfer function between the input ES-AA ratio (for the tibialis anterior (TA ) and gastrocnemius (GC)) and the force output of the endpoint in the ankle joint in an isometric environment by investigating the frequency characteristics, and finally found that the ankle-joint system was a second-order system with dead time in terms of the ES-AA ratio and foot force.

Commentary by Dr. Valentin Fuster
2014;():V002T16A011. doi:10.1115/DSCC2014-6304.

Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). A preceding study indicated that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This preliminary work, as well as the complex nature of the PVL data suggested integration of the active learning algorithm into the overall SVM framework. The present study supports this initial hypothesis and shows that active learning SVM type classifier performs considerably well and outperforms normal SVM type classifiers when dealing with clinical data of high dimensionality.

Commentary by Dr. Valentin Fuster

Dynamics and Control of Wind Energy Systems

2014;():V002T18A001. doi:10.1115/DSCC2014-5907.

Airborne wind energy systems present great promise for inexpensive, clean energy at remote locations, but have only been demonstrated through short-duration flights in very limited wind conditions. Because of the time and money that is required to implement full-scale airborne wind energy prototypes, convergence toward designs that achieve longer-duration flight in adverse weather has been slow. This paper presents an inexpensive rapid prototyping approach for improving the flight dynamics and control of airborne wind energy systems, which has been implemented and validated on Altaeros Energies most recent full-scale flight prototype. The approach involves the 3d printing of lab-scale water channel models of airborne wind energy lifting bodies, which enable prediction of dynamic flight characteristics, rapid iteration between the designs, identification of unknown or poorly known parameters, and improved control design. By applying this approach to its last prototype design cycle, Altaeros demonstrated robust operation in double the wind speeds sustained by its previous prototype (reaching a maximum of 21.2 m/s, with sustained 10–15 m/s winds), with demonstrably improved flight characteristics.

Commentary by Dr. Valentin Fuster
2014;():V002T18A002. doi:10.1115/DSCC2014-5921.

In this paper, an adaptive gain modified optimal torque controller (AGMOTC) is proposed and evaluated for wind turbine partial load operation. An internal PI technique is applied for gain scheduling in order to accelerate the controller response under volatile wind speed while the adaptive searching technique endows the controller with robust convergence to the optimal operating point under plant uncertainties. The light detection and ranging (LIDAR) technology is integrated with the AGMOTC to provide reliable previewed wind speed measurements. Simulations on the NREL 5MW wind turbine show that the LIDAR-enabled AGMOTC outperforms the baseline controller considering the wind energy yield. Additionally, the results show the impact of the proposed controller on the wind turbine fatigue loads.

Commentary by Dr. Valentin Fuster
2014;():V002T18A003. doi:10.1115/DSCC2014-5956.

A wind turbine can experience yawed inflow with large yaw misalignment angle during faulty cases, such as faults in the yaw controller/drives, or during extreme atmospheric cases, such as thunderstorm downbursts. In such cases, it is risky for the turbine to continue operation because it is being exposed to large loads. Instead, it is recommended for the turbine to be transited to parking conditions. Currently, most turbine pitch controllers are designed without considering the yaw misalignment angle, correction of which is normally assigned to the yaw controller. This paper investigates the contribution of both a baseline and a proposed collective pitch controllers under yawed inflow conditions. The baseline controller tries to maintain the rated operating condition at an expense of large blade loads. On the contrary, simulation results show that the proposed controller slows down the turbine under the presence of yawed inflow, which helps to park the turbine and reduces the average blade root bending moments.

Topics: Wind turbines , Inflow
Commentary by Dr. Valentin Fuster
2014;():V002T18A004. doi:10.1115/DSCC2014-5982.

Input/ output feedback linearization and smoothed sliding control methods are used to control a floating offshore wind turbine on a barge platform in high wind speed in order to regulate the power capture. The model of the turbine has the blade pitch angle as the input, generator speed, platform pitch angle and its derivative as the measurements, and wind speed as a disturbance. The designed controllers have been applied to the simplified model of the plant which is used for controller design and also a more complex model which considers all six degrees of freedom for platform movements. Moreover, their performance is compared with the baseline controller for floating offshore wind turbines [1]. Both nonlinear controllers have improved the power fluctuation compared to the baseline controller. Also, sliding control has been shown to have better performance than the input/ output controller, since it can consider the uncertainty of the disturbance signal in the controller design.

Commentary by Dr. Valentin Fuster
2014;():V002T18A005. doi:10.1115/DSCC2014-6064.

Platform stabilization and load reduction are of great importance for the successful development of floating offshore wind turbines. The increased degrees-of-freedom (DOF) for the relevant dynamics presents the challenge of underactuation. Recently, a tuned-mass damper (TMD) and active vane have been proposed to control the pitch and roll motions of a floating turbine platform. Simulations have indicated that TMD in the fore-aft (FA) direction cannot reduce the damage equivalent load (DEQL) for the side-to-side (SS) bending moment at the tower-base across all the loading conditions. In this study, the TMD in the FA direction is combined with an active vertical vane to reduce both the FA and SS platform motions and DEQLs. We refer to this combined system of actuation as the “hybrid actuation system”. The effectiveness of this hybrid scheme is demonstrated via simulations which are carried out in accordance with the IEC 61400-3 standard design load case 1.2–fatigue load testing.

Commentary by Dr. Valentin Fuster

Energy Management Optimization for Conventional and Hybrid Vehicles

2014;():V002T20A001. doi:10.1115/DSCC2014-5998.

Road grade preview can benefit the hybrid electric vehicle (HEV) energy management because the energy efficiency performance degrades significantly when the battery state of charge (SOC) reaches its boundaries and the road grade has a great influence on the battery SOC balance. In reality the road grade in front may be a random variable as the future route may not always be known to the vehicle controller. This paper proposes a stochastic model predictive control (MPC) approach which does not require a determined route known in advance. The road grade is modeled as a Markov chain and all the possible future routes are considered in building the transition matrix. A large-time-scale HEV energy consumption model is built. The HEV energy management problem is formulated as a finite-horizon Markov decision process and solved using stochastic dynamic programming (SDP). Simulation results show that the proposed approach can prevent the battery SOC from reaching its boundaries and maintain good fuel efficiency by the stochastic road grade preview.

Commentary by Dr. Valentin Fuster
2014;():V002T20A002. doi:10.1115/DSCC2014-6009.

Hybrid Electric Vehicle (HEV) is capable of improving fuel economy with reduced emissions over traditional vehicles powered by the internal combustion engine alone. However the HEV durability is significantly limited by the battery useful life; and the battery life could be significantly reduced if it was operated over its allowed charging or discharging limits, which could occur especially at extremely low battery temperatures, leading to permanent battery damage and reduced battery life. In order to extend the battery life, this paper proposed a battery boundary management control strategy based upon the predicted desired torque to proactively make the engine power available to reduce future battery over-discharging. The proposed control strategy was validated in simulations and its performance was compared with the baseline control strategy under US06, and other four typical city and highway driving cycles. The simulation results show that the proposed control strategy is very effective when the battery temperature is under zero Celsius degree, and the over-discharged power is reduced more than 65% under aggressive US06 and ARB02 driving cycles, 45% under highway and city FTP and city NYCC driving cycles, and 30% under highway IM240 driving cycle, respectively.

Commentary by Dr. Valentin Fuster
2014;():V002T20A003. doi:10.1115/DSCC2014-6031.

The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANN-based method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The sensitivity of the prediction precision and computational cost on tuning parameters in examined for each forecasting strategy. Validation results show that the ANN-based velocity predictor exhibits the best overall performance with respect to minimizing fuel consumption.

Commentary by Dr. Valentin Fuster
2014;():V002T20A004. doi:10.1115/DSCC2014-6170.

Dynamic Programming (DP) technique is an effective algorithm to find the global optimum. However when applying DP for finite state problems, if the state variables are discretized, it increases the cumulative errors and leads to suboptimal results. In this paper we develop and present a new DP algorithm that overcomes the above problem by eliminating the need to discretize the state space by the use of sets. We show that the proposed DP leads to a globally optimal solution for a discrete time system by minimizing a cost function at each time step. To show the efficacy of the proposed DP, we apply it to optimize the fuel economy of the series and parallel Hybrid Electric Vehicle (HEV) architectures and the case study of Chevrolet Volt 2012 and the Honda Civic 2012 for the series and parallel HEV’s respectively are considered. Simulations are performed over predefined drive cycles and the results of the proposed DP are compared to previous DP algorithm (DPdis). The proposed DP showed an average improvement of 2.45% and 21.29% over the DPdis algorithm for the series and the parallel HEV case respectively over the drive cycles considered. We also propose a real time control strategy (RTCS) for online implementation based on the concept of Preview Control. The RTCS proposed is applied for the series and parallel HEV’s over the drive cycles and the results obtained are discussed.

Commentary by Dr. Valentin Fuster
2014;():V002T20A005. doi:10.1115/DSCC2014-6286.

Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.

Commentary by Dr. Valentin Fuster
2014;():V002T20A006. doi:10.1115/DSCC2014-6362.

In this paper, we establish a mathematical framework that allows us to optimize the speed profile and select the optimal gears for heavy-duty vehicles. The key idea is to solve the analogous boundary value problem analytically for a simple scenario (linear damped system with quadratic elevation profile) and use this result to initialize a numerical continuation algorithm. Then the numerical algorithm can be used to gradually introduce nonlinearities (air resistance, engine saturation), implement data-based elevation profiles, and incorporate external perturbations (wind, traffic). This approach enables real-time optimization in dynamic traffic conditions, therefore may be implemented on board.

Commentary by Dr. Valentin Fuster

Energy Storage and Optimization

2014;():V002T21A001. doi:10.1115/DSCC2014-5881.

This paper continues the development of a previous conference paper that introduces advanced controllers for an Organic Rankine Cycle (ORC) in a heavy-duty diesel powertrain. The ORC’s heat exchangers are modeled as control-oriented, nonlinear Moving Boundary models. The pump and expander, which are coupled to the engine crankshaft, have relatively faster dynamics than the heat exchangers and are modeled as static components. The driving cycle produces transient heat source and engine conditions for the ORC whose goal is to maximize waste heat recovery under specified operating constraints. This paper describes a Model Predictive Controller (MPC) and compares it to Proportional Integral (PI) and Linear Quadratic Integral (LQI) controllers. The three controllers attempts to regulate heat exchanger pressures while satisfying specified operating constraints. Extended Kalman Filters (EKFs) are designed and implemented for state estimation feedback for MPC and LQI requiring full state feedback. The simulation results show the advantage of the advanced controllers in reducing pressure regulation error, with MPC having the lowest pressure regulation error among the three controllers and able to incorporate the operating constraints into its control law.

Commentary by Dr. Valentin Fuster
2014;():V002T21A002. doi:10.1115/DSCC2014-5933.

Peak power shaving is a technique that can be used to reduce monthly electricity bills. As control of Energy Storage Systems (ESS) is based on predicted power demand, power demand forecasting is a necessary component of entire building power optimization. Various forecasting methods have been developed. However, the importance of intra-day prediction error is overlooked by present models. In this paper, a variety of dynamic intra-day model modification strategies utilizing intra-day prediction error are proposed to improve power demand prediction and peak shaving performance. These modification strategies could be applied to any models which do the prediction at the beginning of the day. A Self-Organizing Map (SOM) & Support Vector Regression (SVR) Adaptive Hybrid Model proposed in previous literature is chosen as baseline in this paper. The method of bisection is adopted to calculate the optimal threshold to control the ESS. Simulation results demonstrate effectiveness of intra-day prediction modification strategies.

Commentary by Dr. Valentin Fuster
2014;():V002T21A003. doi:10.1115/DSCC2014-6131.

This paper proposes a model predictive controller (MPC) using a data-driven thermal sensation model for indoor thermal comfort and energy optimization. The uniqueness of this empirical thermal sensation model lies in that it uses feedback from occupants (occupant actual votes) to improve the accuracy of model prediction. We evaluated the performance of our controller by comparing it with other MPC controllers developed using the Predicted Mean Vote (PMV) model as thermal comfort index. The simulation results demonstrate that in general our controller achieves a comparable level of energy consumption and comfort while eases the computation demand posed by using the PMV model in the MPC formulation. It is also worth pointing out that since we assume that our controller receives occupant feedback (votes) on thermal comfort, we do not need to monitor the parameters such as relative humidity, air velocity, mean radiant temperature and occupant clothing level changes which are necessary in the computation of PMV index. Furthermore simulations show that in cases where occupants’ actual sensation votes might deviate from the PMV predictions (i.e., a bias associated with PMV), our controller has the potential to outperform the PMV based MPC controller by providing a better indoor thermal comfort.

Commentary by Dr. Valentin Fuster
2014;():V002T21A004. doi:10.1115/DSCC2014-6276.

Kinetic energy storage systems for powering vehicles currently exist but are not prevalent. Often the coupling between the flywheel and the vehicle is done using a separate actuator/generator system. This separate actuator system necessarily results in efficiency losses. In this paper we present a design for a spring-coupled variable inertia flywheel which directly couples the flywheel and vehicle. Simulation results for the non-linear dynamic behavior of the system are given and show that it can be used to store more than 99% of the energy of the vehicle when braking, but that there is a tradeoff between device size, deceleration rate, and energy stored. We found that a parameter exploration using three cost functions related to braking time, energy stored, and flywheel radius, shows that one can optimize at most two of the three cost functions. Analytic results are also given for a driven mass-flywheel model, which mitigates some of the problems of the linear spring model. However, this model, if it uses equivalent non-linear springs, is able to store at most 75% of the system energy. The driven-mass/non-linear spring model allows for a lower deceleration and smaller physical size than the linear spring model.

Commentary by Dr. Valentin Fuster
2014;():V002T21A005. doi:10.1115/DSCC2014-6343.

The chilled water system, typically consisting of chiller and cooling tower, plays a major role in the ventilation and air-conditioning systems in commercial buildings. Due to the significant power consumption of such system, improvement of its efficiency would lead to significant benefit in energy saving. As the system characteristics and operational conditions can vary dramatically in practice, model-free self-optimizing control is of high interest in practice. In this study, the chilled-water plant being studied consists of one screw chiller and one counter-flow cooling tower. A multi-variable Newton-based extremum seeking control (ESC) scheme is applied to maximize the power efficiency in real time with the cooling load being satisfied. The feedback for the ESC controller is the total power of the chiller compressor, the cooling tower fan and the condenser water pump, while the inputs are cooling-tower fan speed and the condenser-loop water flow rate. The two-input Newton-based ESC controller is simulated with a Modelica based dynamic simulation model of the chiller-tower system. Two inner-loop PI controllers are used to regulate the temperatures of evaporator superheat and evaporator leaving water at their respective setpoints. Simulation results validate the effectiveness of the proposed control strategy. Remarkable energy saving is observed for several testing conditions.

Topics: Air flow , Water
Commentary by Dr. Valentin Fuster

Energy Storage: Transportation and Grid Applications

2014;():V002T22A001. doi:10.1115/DSCC2014-5980.

Power system operators obtain the flexibility required to reliably balance aggregate generation and load through ancillary service and five-minute energy markets. Market prices are based on the marginal opportunity costs of the generators. This market design works well for generators but inherently fails for storage and demand response, denying these new technologies a fair opportunity to compete and denying the power system access to potentially lower cost reliability resources. Market design or regulatory changes may be required for storage and demand response to be viable ancillary service providers.

Commentary by Dr. Valentin Fuster
2014;():V002T22A002. doi:10.1115/DSCC2014-6022.

Thermostatically controlled loads (TCLs) account for approximately 50% of U.S. electricity consumption. Various techniques have been developed to model TCL populations. A High-fidelity analytical model of heterogeneous TCL populations facilitates the aggregate synthesis of power control in power networks. Such a model assists the utility manager to increase the stability margin of the network. The model, also, assists the customer to schedule his/her tasks in order to reduce his/her energy cost. We present a deterministic hybrid partial differential equation (PDE) model which accounts for heterogeneous populations of TCLs, and facilitates analysis of common scenarios like cold load pick up, cycling, and daily and/or seasonal temperature changes to estimate the aggregate performance of the system. The proposed technique is flexible in terms of parameter selection and ease of changing the set-point temperature and deadband width all over the TCL units. We provide guidelines to maintain the numerical stability of the discretized model during computer simulations. Moreover, the proposed model is a close fit to design output feedback algorithms for power control purposes. Our integral output feedback control, designed using the comparison principle, guarantees fast and efficient power tracking for various real-world scenarios. We present simulation results to verify the effectiveness of the proposed modeling and control technique.

Topics: Stress , Modeling
Commentary by Dr. Valentin Fuster
2014;():V002T22A003. doi:10.1115/DSCC2014-6058.

Non-renewable energy sources such as coal, oil, and natural gas are being consumed at a brisk pace while greenhouse gases contribute to atmospheric pollution. A global shift is underway toward the inclusion of renewable energy sources, such as solar and wind, for generating electrical and mechanical power. To meet this emerging demand, a solar based electrical microgrid study is underway at Clemson University. Solar energy is harvested from photovoltaic panels capable of producing 15 kW of DC power. Compressed air energy storage has been evaluated using the generated solar power to operate an electric motor driven piston compressor. The compressed air is then stored under pressure and supplied to a natural gas driven Capstone C30 MicroTurbine with attached electric power generator. The compressed air facilitates the turbine’s rotor-blade operated compression stage resulting in direct energy savings. A series of mathematical models have been developed. To evaluate the feasibility and energy efficiency improvements, the experimental and simulation results indicated that 127.8 watts of peak power was delivered at 17.5 Volts and 7.3 Amps from each solar panel. The average power generation over a 24-hour time period from 115 panels was 15 kW DC or 6 kW of AC power at 208/240 VAC and 25 Amps from the inverter. This electrical power could run a 5.2 kW reciprocating compressor for approximately 5 hours storing 1,108 kg of air at a 1.2 MPa pressure. A case study indicated that the microturbine, when operated without compressed air storage, consumed 11.2 kg of gaseous propane for 30 kW·hr of energy generation. In contrast, the microturbine operated in conjunction with solar supplied air storage could generate 50.8 kW·hr of electrical energy for a similar amount of fuel consumption. The study indicated an 8.1% efficiency improvement in energy generated by the system which utilized compressed air energy storage over the traditional approach.

Commentary by Dr. Valentin Fuster
2014;():V002T22A004. doi:10.1115/DSCC2014-6099.

A Compressed Air Energy Storage (CAES) test-bed has been developed to experimentally demonstrate the energy storage concept proposed in [1] for offshore wind turbines. The design of the testbed has been adapted to the available air compression/expansion technology. The main components of the system consist of an open accumulator, a hydraulic pumpmotor, air compressor/expander, an electrical generator and load, a differential gearbox and a hydraulic control valve. These components are first characterized and then a dynamic model of the system has been developed. The objective is to regulate the output current/voltage of the generator while maintaining a constant accumulator pressure in the presence of input and demand power variations in the system. This is achieved by Proportional-Integrator (PI) control of pumpmotor displacement and field current of the generator. The stability of these controllers has been proved using an energy-based Lyapunov function. Experimental results for storage and regeneration modes have been presented showing excellent performance of the system in response to power disturbances.

Commentary by Dr. Valentin Fuster
2014;():V002T22A005. doi:10.1115/DSCC2014-6113.

The purpose of this research is to the problem of optimal sizing of energy storage required for compensation of wind farm generation variability. Using wind farm production data from the BPA, we assess the effect of forecast quality and economic dispatch timing on the size of storage and critical power rating required to nearly perfectly match the committed energy. We develop a Model-Predictive-Control (MPC) based operational model following NERC standard recommendations. Different forecasts are considered and compared from the storage sizing perspective. The results of our simulations can be fit by two simple relations, connecting the storage sizing with forecast error, wind variability, and the timescales of scheduling. A more accurate forecast reduces the storage sizing. However, diminishing returns are observed when the forecast error becomes comparable to natural wind variability within the commitment time interval. The proposed methodology can be extended to other systems with intermittent generation and controllable real or virtual storage.

Topics: Energy storage
Commentary by Dr. Valentin Fuster
2014;():V002T22A006. doi:10.1115/DSCC2014-6127.

Research into heating, ventilation, and air conditioning systems has shown that coordinating building climate control leads to large energy savings. However, most analyses have assumed linear dynamics not reflective of actual systems. Using a cascaded control architecture, this linear behavior can be recovered, allowing for maximum energy savings to be realized. Case studies on variable air volume, hydronic radiator, and terminal fan controlled systems demonstrate the broad application and benefits of this approach. Also, because of the architectural simplicity and lack of required a priori knowledge of system performance or characteristics, the cascaded controller can be implemented immediately in the HVAC community.

Topics: HVAC equipment
Commentary by Dr. Valentin Fuster

Energy Storage: Transportation Applications

2014;():V002T23A001. doi:10.1115/DSCC2014-5986.

Accurate real-time knowledge of battery internal states and physical parameters is of the utmost importance for intelligent battery management. Electrochemical models are arguably more accurate in capturing physical phenomena inside the cells compared to their data-driven or equivalent circuit based counterparts. Moreover, consideration of the coupling between electrochemical and thermal dynamics can be potentially beneficial for accurate estimation. In this paper, a nonlinear adaptive observer design is presented based on a coupled electrochemical-thermal model for a Li-ion cell. The proposed adaptive observer estimates distributed Li-ion concentrations, lumped temperature and some electrochemical parameters simultaneously. The observer design is split into two separate parts to simplify the design procedure and gain tuning. These separate parts are designed based on Lyapunov’s stability analysis guaranteeing the convergence of the combined state-parameter estimates. Simulation studies are provided to demonstrate the effectiveness of the scheme.

Topics: Design
Commentary by Dr. Valentin Fuster
2014;():V002T23A002. doi:10.1115/DSCC2014-6059.

In this paper an active excitation approach to battery model parameter identification is discussed. Based on begin-of-life battery model, it is possible to establish a reference parameter table (either fixed, or adaptively learned), and based on such reference parameter table, as well as by analysing battery input signal, active excitation request may be generated. Active excitation is achieved based on maintaining overall torque level with regard to drive input, while adjusting both engine and battery power output (and input). Both conditions for active excitation request, as well as active excitation generation approaches, are presented in detail. Simulation examples using production electrified vehicle battery model parameters and real world drive cycles demonstrate that the proposed approach indeed improves battery model parameter identification accuracy.

Topics: Batteries
Commentary by Dr. Valentin Fuster
2014;():V002T23A003. doi:10.1115/DSCC2014-6061.

A switching adaptive observer is proposed for estimation of state of charge (SOC) for lithium ion batteries used in electrified automotive propulsion systems. The base observer includes (i) a parameter estimation subsystem including a recursive parameter estimator for identifying battery parameters and (ii) an open circuit voltage (OCV) estimation subsystem including a nonlinear adaptive observer for estimating battery OCV. A timer as well as excitation level determination decides when the ampere-hour integration based SOC or estimated OCV based SOC is used as output. Using this approach, transient response of the adaptive SOC estimator is greatly improved. Examples are used to show the effectiveness of the proposed approach.

Commentary by Dr. Valentin Fuster
2014;():V002T23A004. doi:10.1115/DSCC2014-6082.

Lithium-ion batteries for automotive applications are subject to aging with usage and environmental conditions, leading to the reduction of the performance, reliability and life span of the battery pack. To this extent, the ability of simulating the dynamic behavior of a battery pack using high-fidelity electrochemical and thermal models could provide very useful information for the design of Battery Management Systems (BMS). For instance such models could be used to predict the impact of cell-to-cell variations in the electrical and thermal properties on the overall performance of the pack, as well as on the propagation of degradation from one cell to another.

This paper presents a method for fast simulation of an integrated electrochemical-thermal battery pack model based on first-principles. First, a coupled electrochemical and thermal model is developed for a single cell, based upon the data of a composite LiNi1/3Mn1/3Co1/3O2 – LiMn2O4 (LMO-NMC) Li-ion battery, and validated on experimental data. Then, the cell model is extended to a reconfigurable and parametric model of a complete battery pack. The proposed modeling approach is completely general and applicable to characterize any pack topology, varying electrical connections and thermal boundary conditions. Finally, simulation results are shown to illustrate the effects of parameter variability on the pack performance.

Commentary by Dr. Valentin Fuster
2014;():V002T23A005. doi:10.1115/DSCC2014-6176.

This paper presents a method for estimating (i) the reciprocal of the thermal time constant of a lithium-ion battery cell and (ii) the cell’s entropy coefficients for different states of charge. The method utilizes dynamic battery temperature cycling for parameter estimation. The paper demonstrates this method specifically for a cylindrical lithium iron phosphate (LiFePO4) cell. Identifying battery thermal parameters is important for accurate thermo-electrochemical modeling and model-based battery management. Entropy coefficients have been identified in previous research for various battery chemistries using calorimetric and potentiometric measurements requiring quasi-equilibrium conditions. This work, in contrast, fits the entropy coefficients and the reciprocal of the thermal time constant of a first-order thermal model to datasets collected in a noninvasive, dynamic experiment. This reduces the time required for parameter identification by a factor of 3 compared to traditional quasi-equilibrium experiments.

Topics: Entropy , Batteries
Commentary by Dr. Valentin Fuster
2014;():V002T23A006. doi:10.1115/DSCC2014-6321.

The temperature distribution in a prismatic Li-ion battery cell can be described using a spatially distributed equivalent circuit electrical model coupled to a 3D thermal model. The model represents a middle ground between simple one or two state models (generally used for cylindrical cells) and complex finite element models. A lumped parameter approach for the thermal properties of the lithium-ion jelly roll is used. The battery is divided into (m × n) nodes in 2-dimensions, and each node is represented by an equivalent circuit and 3 temperatures in the through plane direction to capture the electrical and thermal dynamics respectively. The thermal model is coupled to the electrical through heat generation. The parameters of the equivalent circuit electrical model are temperature and state of charge dependent. Parameterization of the distributed resistances in the equivalent circuit model is demonstrated using lumped parameter measurements, and are a function of local temperature. The model is parameterized and validated with data collected from a 3-cell fixture which replicates pack cooling conditions. Pulsing current experiments are used for validation over a wide range of operating conditions (ambient temperature, state of charge, current amplitude and pulse width). The model is shown to match experimental results with good accuracy.

Commentary by Dr. Valentin Fuster

Estimation and Identification Methods

2014;():V002T24A001. doi:10.1115/DSCC2014-5837.

The characterization of complex flows is often based on kinetic and kinematic measurements computed from high dimensional sets of data. Computationally intensive processing of such large scale data sets is a major challenge in climatological and microfluidic applications. Here, we offer a novel approach based on noninvasive and unsupervised analysis of fluid flows through nonlinear manifold learning. Specifically, we study varying flow regimes in the wake of a circular cylinder by acquiring experimental video data with digital cameras and analyze the video frames with the isometric feature mapping (Isomap). We show that the topology of Isomap embedding manifolds directly captures inherent flow features without performing velocity measurements. Further, we establish relationships between the amount of embedded data and the Reynolds number, which are utilized to detect the flow regime of independent experiments.

Topics: Flow (Dynamics)
Commentary by Dr. Valentin Fuster
2014;():V002T24A002. doi:10.1115/DSCC2014-5865.

Information-theoretical notions of causality provide a model-free approach to identification of the magnitude and direction of influence among sub-components of a stochastic dynamical system. In addition to detecting causal influences, any effective test should also report the level of statistical significance of the finding. Here, we focus on transfer entropy, which has recently been considered for causality detection in a variety of fields based on statistical significance tests that are valid only in the asymptotic regime, that is, with enormous amounts of data. In the interest of applications with limited available data, we develop a non-asymptotic theory for the probability distribution of the difference between the empirically-estimated transfer entropy and the true transfer entropy. Based on this result, we additionally demonstrate an approach for statistical hypothesis testing for directed information flow in dynamical systems with a given number of observed time steps.

Commentary by Dr. Valentin Fuster
2014;():V002T24A003. doi:10.1115/DSCC2014-5877.

This paper provides a method to design an optimal switching sequence for jump linear systems with given Gaussian initial state uncertainty. In the practical perspective, the initial state contains some uncertainties that come from measurement errors or sensor inaccuracies and we assume that the type of this uncertainty has the form of Gaussian distribution. In order to cope with Gaussian initial state uncertainty and to measure the system performance, Wasserstein metric that defines the distance between probability density functions is used. Combining with the receding horizon framework, an optimal switching sequence for jump linear systems can be obtained by minimizing the objective function that is expressed in terms of Wasserstein distance. The proposed optimal switching synthesis also guarantees the mean square stability for jump linear systems. The validations of the proposed methods are verified by examples.

Commentary by Dr. Valentin Fuster
2014;():V002T24A004. doi:10.1115/DSCC2014-5997.

The objective of this paper is to present a methodology to modularly connect Multi-Layer Perceptron (MLP) neural network models describing static port-based physical behavior. The MLP considered in this work are characterized for an standard format with a single hidden layer with sigmoidal activation functions. Since every port is defined by an input-output pair, the number of outputs of the proposed neural network format is equal to the number of its inputs. This work extends the Model Assembly Method (MAM) used to connect transfer function models and Volterra models to multi-layer perceptron neural networks.

Commentary by Dr. Valentin Fuster
2014;():V002T24A005. doi:10.1115/DSCC2014-6024.

This article discusses the challenges of non-intrusive state measurement for the purposes of online monitoring and control of Ultraviolet (UV) curing processes. It then proposes a two-step observer design scheme involving the estimation of distributed temperature from boundary sensing cascaded with nonlinear cure state observers. For the temperature observer, backstepping techniques are applied to derive the observer partial differential equations along with the gain kernels. For subsequent cure state estimation, a nonlinear observer is derived along with analysis of its convergence characteristics. While illustrative simulation results are included for a composite laminate curing application, it is apparent that the approach can also be adopted for other UV processing applications in advanced manufacturing.

Commentary by Dr. Valentin Fuster

Estimation and Tracking

2014;():V002T25A001. doi:10.1115/DSCC2014-5979.

An adaptive sliding mode spacecraft attitude controller is derived in this paper. It has the advantage of not requiring knowledge of the inertia of the spacecraft, and rejecting unexpected external disturbances, with global asymptotic position and velocity tracking. The sliding manifold is designed using optimal control analysis of the quaternion kinematics. The sliding mode control law and the parameter adaptation law are designed using Lyapunov stability. Numerical simulations are performed to demonstrate both the nominal and the robust performance.

Topics: Space vehicles
Commentary by Dr. Valentin Fuster
2014;():V002T25A002. doi:10.1115/DSCC2014-5981.

A marginalized particle filter (MPF) is designed for attitude estimation problem. Unit quaternions are used to parameterize rotations. The linear structure in the gyroscope bias dynamics enables us to completely decouple its evolution from quaternion particles. We further show that the linear part of the proposed MPF reaches a steady state, similar to what Kalman filter does for controllable and observable linear stochastic systems. Although the steady-state MPF is similar to the particle filter in structure, it has two advantages: (i) the theoretical superiority of marginalizing linear substructure, and (ii) the reduction in total computational time. Numerical simulations are performed to demonstrated the performance of the proposed filter.

Commentary by Dr. Valentin Fuster
2014;():V002T25A003. doi:10.1115/DSCC2014-6232.

In this paper, we present an optimal sensor manager and a path planner for an Unmanned Aerial Vehicle (UAV) to geo-localize multiple mobile ground targets. A gimbaled camera with a limited field of view (FOV) and a limited range is used to capture targets, whose states are estimated using a set of Extended Kalman Filters (EKFs). The sensor management is performed using a dynamic weighted graph and a Model Predictive Control (MPC) technique, determining the optimal gimbal pose that minimizes the overall uncertainty of target states. A UAV path planner that maximizes a novel cost function is employed to support the sensor management. Simulation results show the effectiveness of the proposed sensor manager and the path planner.

Commentary by Dr. Valentin Fuster
2014;():V002T25A004. doi:10.1115/DSCC2014-6235.

This paper investigates the variable interval sampling based time-varying tracking control in the rotational angle domain. It is found that more sampling points per revolution provide better tracking performance but increase the computational burden. To solve the problem, a varying interval sampling approach is presented to optimize the angular sampling interval for the reference profile, while maintaining the same total number of sampling points. The tracking performance is improved by considering the tracking errors between the sampling points in selecting the optimal sampling intervals. Experimental results from a time-varying internal model based camless engine valve actuation system demonstrate the effectiveness of the proposed method. A quantitative analysis helps to highlight the strength of the variable interval sampling on less computational complexity and better tracking performance.

Commentary by Dr. Valentin Fuster
2014;():V002T25A005. doi:10.1115/DSCC2014-6316.

In this article, the problem of trajectory design and tracking of non-periodic tracking-transition switching for non-minimum phase linear systems is considered. Such a trajectory design and tracking problem exists in applications such as nanomanipulation, robotic operation, and hard-disk control, where the whole trajectory consists of tracking sessions with the application-specified desired trajectory to be tracked, and transition sessions with the output trajectory to be designed. This problem becomes challenging as multiple control objectives need to be achieved. The proposed approach extends the previous work that attained smooth output transition and smooth tracking-transition switching to further achieve amplitude-constrained input-energy minimization and transition time minimization. First, the constrained input optimization problem is converted to an unconstrained input minimization problem. Then the optimal output and input are obtained by using an improved conjugate gradient method. Finally, the total transition time is further minimized via one dimensional search. The proposed approach is illustrated through a simulation example in probe-based nanomanipulation utilizing a piezoelectric actuator.

Topics: Linear systems
Commentary by Dr. Valentin Fuster
2014;():V002T25A006. doi:10.1115/DSCC2014-6336.

Problem of autonomous vehicle platooning in an automated highway setting has drawn many attentions, both in academia and industry, during last two decades. This paper studies the problem of vehicle platooning with a particular focus on merging control algorithm when one or several vehicle(s) merge(s) from the adjacent lane into the main vehicle platoon under longitudinal control. Different longitudinal controllers have been compared. A practical novel multi-vehicle merge-in strategy and an adaptive lateral trajectory generation method have been proposed. The proposed approach is then tested and verified in our newly developed simulation platform SimPlatoon.

Commentary by Dr. Valentin Fuster

Estimation, Detection and Tracking

2014;():V002T26A001. doi:10.1115/DSCC2014-5863.

In this paper, performance comparisons are carried out between two out-of-sequence estimation filtering techniques based on the principles of the Extended Kalman Filter (EKF) and the Sigma-point Kalman filter (SPKF), in a mobile platform tracking application where distributed radars are used to estimate both linear and highly nonlinear movements of an aircraft. Two scenarios were considered: 1) aircraft movements fit a white noise acceleration model; and 2) aircraft movement follows a coordinated turn model with unknown turn rate. In addition, we evaluate the individual performance of the out-of-order filters against the ideal cases obtained by running the EKF and SPKF with reordered measurements in a chronological sequence. Simulation results show that the algorithms used for dealing with out-of-sequence measurements closely resemble the performance of the non-out-of-order filters. In terms of estimation accuracy, the out-of-order algorithm based on the SPKF outperforms the one based on the EKF when a highly nonlinear aircraft movement is observed. For nearly linear systems, there is not a significant difference between the two approaches.

Commentary by Dr. Valentin Fuster
2014;():V002T26A002. doi:10.1115/DSCC2014-5866.

Target tracking scenarios offer an interesting challenge for state and parameter estimation techniques. This paper studies a situation with multiple targets in the presence of clutter. In this paper, the relatively new smooth variable structure filter (SVSF) is combined with the joint probability data association (JPDA) technique. This new method, referred to as the JPDA-SVSF, is applied on a simple multi-target tracking problem for a proof of concept. The results are compared with the popular Kalman filter (KF).

Commentary by Dr. Valentin Fuster
2014;():V002T26A003. doi:10.1115/DSCC2014-5889.

In this research, a variety of Kalman Filters are implemented in an effort to estimate sled speed of a Roll Simulator. An Extended Kalman Filter (EKF) is incorporated to capture the nonlinear dynamics of the sled-platform assembly to estimate sled speed for the entire motion, as a linear Kalman Filter was found to be inadequate. When applied to experimental data, the EKF over-estimates sled speed, which is due to a disturbance force and/or uncertainty in system parameters. In combination with the disturbance observer, the Kalman Filter adequately estimates sled speed for experimental data. For lower speed/payload applications, a Kalman Filter using an accelerometer and measured drum speed is able to accurately track sled speed when a gain scheduling scheme is employed.

Commentary by Dr. Valentin Fuster
2014;():V002T26A004. doi:10.1115/DSCC2014-6242.

With the advent of terrain surface mapping capabilities comes the necessity to extract useful information from the copious data, particularly localized events. Digital image processing methods for edge detection are applied to road surfaces to locate localized road events. A novel method of edge detection is developed based on the Nodal Uncertainty in which the probability distribution of the nodal heights determines the edges of an event. An example demonstrates that the new method performs at least as well as the best digital image processing methods available. Future development of this work is planned for integration with event characterization and identification methods.

Topics: Uncertainty
Commentary by Dr. Valentin Fuster
2014;():V002T26A005. doi:10.1115/DSCC2014-6361.

Cybersecurity for networked industrial control systems presents challenges not faced in information technology systems. The use of heritage protocols, such as Modbus, on unrouted serial buses makes it difficult to authenticate actuator commands and sensor data. Furthermore, rigid master/slave architectures such as Modbus are especially vulnerable to compromise of the master unit. We describe a logic program called the Qualitative Behavioral Analyzer (QBA) for monitoring a controlled physical process on an unrouted Modbus network. The proposed approach uses knowledge of process physics to identify a possible component fault or cyberattack. To avoid relying on the integrity of the master unit, the QBA directly analyzes Modbus network traffic. The first stage of the QBA is called the Network Analyzer, which evaluates each Modbus packet and extract qualitative information. The second stage is called the Physics Analyzer, which evaluates a qualitative physics model based on qualitative information from Network Analyzer. The QBA is demonstrated on simulations of a water treatment process.

Commentary by Dr. Valentin Fuster
2014;():V002T26A006. doi:10.1115/DSCC2014-6365.

Anomalies in cyber-physical systems may arise due to malicious cyber attacks or operational faults in the physical devices. Accurately detecting the anomalies and isolating their root-causes is important for identifying appropriate reactive and preventive measures and building resilient cyber-physical systems. Anomaly detection and isolation in cyber-physical systems is challenging, because the impact of a cyber attack on the operation of a physical system may manifest itself only after some time. In this paper, we present a Bayesian network approach for learning the causal relations between cyber and physical variables as well as their temporal correlations from unlabeled data. We describe the data transformations that we performed to deal with the heterogeneous characteristics of the cyber and physical data, so that the integrated dataset can be used to learn the Bayesian network structure and parameters. We then present scalable algorithms to detect different anomalies and isolate their respective root-cause using a Bayesian network. We also present results from evaluating our algorithms on an unlabeled dataset consisting of anomalies due to cyber attacks and physical faults in a commercial building system.

Commentary by Dr. Valentin Fuster

Ground and Space Vehicle Dynamics

2014;():V002T27A001. doi:10.1115/DSCC2014-5964.

Using feedback information of estimates from a model of the hydraulic clutch actuation and measurements from transmission mechanicals, a closed-loop adaptive controller is designed. The controller is structured to update at three different rates: every time instance, every shift, and every n-th number of shifts. Part of the controller is designed to operate in open-loop for the first two regions of the shift until feedback information is available. The open-loop controller adapts within the shift, thus allowing for corrections to the control design to be made during the current shift and in subsequent shifts. The model tuning parameters as well as the return spring pre-load force become the adaptive parameters, which are being adjusted so that the plant matches the model in real-time operation. The control design is validated against a high fidelity simulation model of the transmission hydraulics and mechanicals, as well as experimental data.

Commentary by Dr. Valentin Fuster
2014;():V002T27A002. doi:10.1115/DSCC2014-5978.

A new method for road profile estimation in time domain with the application of vehicle system response was presented in this paper, and the problem was transformed as a system identification issue for an inverse nonlinear quarter vehicle model. Firstly, the inverse vehicle dynamic model was trained with specifically chosen white noise signal, and then eight different types of membership functions (MF) for Adaptive Neuro Fuzzy Inference System (ANFIS) were compared. Finally, the comparison of three different methods: ANFIS, Recursive Least Square (RLS) and Group Method of Data Handling (GMDH) were researched with different vehicle speeds and different road levels in the simulation part. The results showed that ANFIS is better in comparison with RLS and GMDH and this method can be further applied for vehicle system analysis.

Commentary by Dr. Valentin Fuster
2014;():V002T27A003. doi:10.1115/DSCC2014-6086.

Most of the previous research in the field of power-split hybrid electric vehicles focused on the powertrain topology optimization. However, depicting a given or found topology in the form of schematic diagram, required for the advanced steps of vehicles’ design, has not yet been studied. In this paper, we propose a systematic approach to automatically generate all feasible stick diagrams for all twelve split-hybrid powertrain topologies with a single planetary gear (PG). The stick diagram is a simplified cartoon layout that schematically illustrates the connections, arrangements, and positions of the powertrain components. The proposed process is divided into three steps. First, we introduce the placement diagram, which specifies the position of the components with respect to the planetary gear. Secondly, for each placement diagrams, all positioning diagrams are generated where the relative location of each component is determined. The use of positioning diagrams guarantees dealing with all the possible arrangements. Lastly, the feasible stick diagrams are selected by filtering out infeasible ones from the entire pool of candidate stick diagrams using a set of feasibility rules. The proposed method is used for several topologies, such as Toyota Prius and GM Volt, and it is found that the patented stick diagrams are a subset of all the feasible stick diagrams. Therefore, one can systematically generate all the feasible stick diagrams for any given single PG powertrain topology using the proposed design methodology.

Commentary by Dr. Valentin Fuster
2014;():V002T27A004. doi:10.1115/DSCC2014-6137.

Leg-wheel architectures for locomotion systems offer many advantages, not the least of which is reconfigurability of wheel-axle with respect to the chassis. Thus, locomotion systems with multiple leg-wheels now permit enormous reconfigurability of the chassis frame with respect to the ground frame. We seek to systematically exploit this ability to reconfigure within this highly-redundant system to enhance contact kinematics i.e., reducing the slippage and improving traction forces at wheel-ground interfaces. In addition, reconfiguration can also be used to mitigate undesirable system-level effects (such as judder) and lead to greatly improved estimation for navigation. In this paper, we examine a systematic analytical approach to the modeling, analysis and reconfiguration of articulated leg-wheel systems, to enhance both traction as well as stability-margin, while navigating over rough-terrains. The derivations will also be specialized to a particular example of an ultra-mobile actively-articulated vehicle to illustrate the developed procedure.

Topics: Vehicles
Commentary by Dr. Valentin Fuster
2014;():V002T27A005. doi:10.1115/DSCC2014-6301.

Active steering systems allow for improved vehicle safety and stability through steering interventions that augment a driver’s steering command. In a conventional steering system, steering feedback torque depends on the tire forces and corresponding moments that act on the roadwheels. During active steering interventions, there are differences between the driver’s command and the actual roadwheel angle. The steering feedback can now be based on either the moments acting on the actual roadwheels or the moments acting on a virtual wheel following the driver’s intended steering command. With small interventions, the difference between these two approaches is negligible. However, when the intervention is large (e.g. obstacle avoidance maneuvers), basing handwheel moments on the actual roadwheel position results in a handwheel torque that acts in opposition to the intervention. The virtual wheel concept produces a more supportive, and potentially more intuitive, handwheel torque. This reduces the discrepancy between the driver command and the active steering system in simulation and experiments.

Topics: Feedback , Wheels
Commentary by Dr. Valentin Fuster
2014;():V002T27A006. doi:10.1115/DSCC2014-6315.

The analysis of recent and classical on-off controllers for semi-active suspensions are presented. The research is supported by a realistic platform consisting of an industrial ECU based on a controller area network (CAN) interfacing a real time simulation system. Two experimental models, quarter of a vehicle and semi-active (SA) damper allow to represent the realistic behavior of the vehicle and electrical/network parts. The proposed non-linear model of the SA damper permits its inclusion in the real time simulator without computing issues and valid damper characteristics. A qualitative and quantitative analysis of each controller is proposed in this paper oriented to real time implantation. The requirement for the sampling time for the analyzed controllers is discussed.

Commentary by Dr. Valentin Fuster

Intelligent Systems Control

2014;():V002T29A001. doi:10.1115/DSCC2014-5824.

In this paper, Jeffcott rotor model is employed to explore the vibration response of breathing cracked system with unbalance mass. Based on the energy method and Lagrange principle, 6 degree-of-freedom equation of motion is derived in fixed coordinate system. The crack model is established using strain energy release theory of facture mechanics. The stiffness matrix induced by the crack is changing with the variation of crack open area. Zero stress intensity factor (SIF) method is used to determine the crack closure line by computing the SIF for opening mode. By integrating compliant coefficients over newly determined crack open area, the stiffness matrix is updated and vibration response is solved for every time step by Gear’s method. In addition, the breathing behavior of the crack is studied for multiple eccentricity phases and rotation speeds in order to provide effective guidance for damage detection. The paper explores the effect of external torsional loading on the crack breathing behavior. Finally, the coupling of lateral and torsional vibration is investigated to be used as an indicator of damage detection and health monitoring.

Commentary by Dr. Valentin Fuster
2014;():V002T29A002. doi:10.1115/DSCC2014-5842.

Biomimetic robotic fish exhibits have been an attraction for many visitors in informal learning settings. Although these exhibits are entertaining to the visitors, they generally lack interactive components to promote participants’ engagement. Interactivity in exhibits is an increasing trend in public educational venues, and is a crucial factor for promoting science learning among participants. In this work, we propose a novel platform for enhancing participant interaction through a robotic fish controlled by a touch screen device. Specifically, we develop and characterize a robotic fish based on a multi-link design with a pitch and buoyancy control system for three-dimensional biomimetic swimming. Performance tests are conducted to assess the robotic fish speed.

Commentary by Dr. Valentin Fuster
2014;():V002T29A003. doi:10.1115/DSCC2014-5896.

Mechanical characterization of thin samples is now routine due to the prominence of the Atomic Force Microscope. Advances in amplitude modulation techniques have allowed for accurate measurement of a sample’s elastic properties by interpreting the changes in the vibration of a cantilevered beam in intermittent contact. However, the nonlinearities associated with contact complicate attempts to find an accurate time-history for the beam. Furthermore, the inclusion of viscous effects, common to soft samples, puts an explicit solution even farther from reach.

A numerical method is proposed that analyzes the time-history and frequency response of a microcantilever beam with a viscoelastic end-condition. The mathematics can be simplified by incorporating the viscoelastic end-condition into the equation of motion directly by modeling it as a distributed load. A forcing function can then be derived from the Standard Linear Solid model of viscoelasticity and implemented in the non-conservative work term of Hamilton’s principle. The Galerkin method can separate the resulting nonlinear equation of motion into time and space components. Performing a numerical analysis of the time factor equation provide the beam’s response over time. The results demonstrate the distinctive effects of viscoelasticity and periodic contact on the beam’s motion and provide the framework for the determination of viscous properties using dynamic techniques.

Commentary by Dr. Valentin Fuster

Intelligent Transportation Systems

2014;():V002T30A001. doi:10.1115/DSCC2014-5884.

A number of automotive crashes occur each year due to semitrailers following passenger vehicles too closely on interstate highways and secondary roads. This hazardous practice, called tailgating, accounted for over 40% of the 110,000 trailer-passenger vehicle crashes recorded by the National Highway Traffic Safety Administration (NHTSA) in 2010. Tailgating is difficult to detect and document using visual methods and law enforcement agencies must depend on trained officers, whose abilities may be limited. In this paper, a proposed tailgating detection system, mounted to the officer’s patrol vehicle, continuously monitors both passenger and commercial vehicles, as the officer travels down the roadway. A rotating laser range-finding sensor feeds information to a microprocessor that continuously searches for the occurrence of tailgating. A weighting algorithm determines when a tailgating event has definitively occurred to reduce system sensitivity. If an event is detected, the officer is notified with audio and visual cues. A time stamped record including all relevant system information for later use in legal prosecution is also produced. In a virtual case study, the computer generated roadway environment was populated with vehicles of varying velocity and location. The numerical results show that the detection algorithm was able to successfully locate all of the virtual vehicles and accurately determine tailgating events under a number of different simulation conditions.

Commentary by Dr. Valentin Fuster
2014;():V002T30A002. doi:10.1115/DSCC2014-6118.

The iterative algorithm of design variables for structural topology optimization is derived by using variable density approach and Finite Element Method. A coupled model of bent-bar-frame piston is built considering the contact between piston and cylinder, piston and piston pin, piston pin and connecting rod. Based on this model, the deformation and stress of piston are analyzed under each of mechanical or thermal loading. Taking structural weight as the objective function of optimization, three desired regions of piston are optimized by using variable density approach in commercial FEA software HYPERMESH and ANSYS. Finally, the deformation and temperature of the optimized model are compared with prototype by using the same loading and boundary conditions. The results show that the weight of piston is reduced by 12.5% while meeting the required specifications.

Commentary by Dr. Valentin Fuster
2014;():V002T30A003. doi:10.1115/DSCC2014-6208.

Iterative learning control is an adaptive, feedforward control technique traditionally used to improve the performance of systems that execute a task repetitively. While generally applied to systems driven by temporal dynamics, there exist applications, such as additive manufacturing, for which spatial dynamics play a particularly important role in determining system behavior. To ensure high fidelity functionality for these application spaces, this paper presents a spatial learning framework for optimizing multiple performance metrics simultaneously. Utilizing a one-step optimization approach enables direct evaluation of design trade-offs over a broad range of potential solutions. The multi-objective spatial learning framework, along with stability and convergence analysis is presented. Simulation results validate the control framework.

Commentary by Dr. Valentin Fuster
2014;():V002T30A004. doi:10.1115/DSCC2014-6255.

In motion systems, high controller gains are beneficial in order to suppress disturbances acting on the system. Low-damped non-rigid body (NRB) resonances usually limit this controller gain. The result is a bound on the maximum achievable sensitivity, i.e. the suppression of low frequent position disturbances. Robust Mass Dampers (RMD’s) with a relatively high damping value have shown to be able to increase the NRB damping over a broad frequency range. The main difficulty is to determine the stiffness and damping parameters for these damper mechanisms in order to optimize the closed loop performance of the motion system. This paper proposes a modulus margin based iterative optimization procedure which includes a plant model with dampers added and a PID+ type controller. The results are optimal damper parameters — stiffness and damping — in combination with an as high as possible controller gain, which result in an improved disturbance suppression at frequencies below the bandwidth and a faster setpoint tracking.

Commentary by Dr. Valentin Fuster
2014;():V002T30A005. doi:10.1115/DSCC2014-6262.

This paper proposes a framework for autonomous vehicles to collaborate with human agents as peers in task completion scenarios. In this framework, the autonomous vehicles utilize the Bayesian inference method to determine human intention. An optimal task allocation that minimizes the mission completion time while respecting the intention of the human agents is developed using the Mixed Integer Linear Programming (MILP) method. The proposed framework can accommodate different levels of suboptimality in human agents’ behavior by adjusting a tunable parameter in the inference model. The effectiveness of the framework in facilitating human-autonomous vehicle collaboration is demonstrated through simulations.

Commentary by Dr. Valentin Fuster
2014;():V002T30A006. doi:10.1115/DSCC2014-6269.

The dynamics of an autonomous unmanned ground vehicle (UGV) that is at least the size of a passenger vehicle are critical to consider during obstacle avoidance maneuvers to ensure vehicle safety. Methods developed so far do not take vehicle dynamics and sensor limitations into account simultaneously and systematically to guarantee the vehicle’s dynamical safety during avoidance maneuvers. To address this gap, this paper presents a model predictive control (MPC) based obstacle avoidance algorithm for high-speed, large-size UGVs that perceives the environment only through the information provided by a sensor, takes into account the sensing and control delays and the dynamic limitations of the vehicle, and provides smooth and continuous optimal solutions in terms of minimizing travel time. Specifically, information about the environment is obtained using an on-board Light Detection and Ranging (LIDAR) sensor. Ensuring the vehicle’s dynamical safety is translated into avoiding single tire lift-off. The obstacle avoidance problem is formulated as a multi-stage optimal control problem with a unique optimal solution. To solve the optimal control problem, it is transcribed into a nonlinear programming (NLP) problem using a pseudo-spectral method, and solved using the interior-point method. Sensing and control delays are explicitly taken into consideration in the formulation. Simulation results show that the algorithm is capable of generating smooth control commands to avoid obstacles while guaranteeing dynamical safety.

Commentary by Dr. Valentin Fuster

Mechatronics for Energy Harvesting

2014;():V002T32A001. doi:10.1115/DSCC2014-6071.

A novel electromagnetic transducer shunt circuit is proposed in this paper for dual-functional energy harvesting and vibration control of building seismic isolation. In recent decades, base isolation systems are widely used in low and middle rise buildings. Even though base isolation can filter out high frequency excitation from earthquake, it still necessary to consider higher order modes’ vibration in host structure. The new design extends the multi-mode shunt circuit technology in piezoelectric area in order to achieve good vibration suppression into the seismic isolation of multi degree of freedoms (MDOF) of host structure of buildings, and use multi-mode circuit to achieve both energy harvesting and seismic vibration control. A numerical study of simplified two degree of freedom base isolation is presented in this paper. This passive system is also examined by giving recorded earthquake excitation. The stimulation results show that this new design could take advantage both of low-pass filtering capacity of base isolation system and resonant vibration reduction of electromagnetic shunt circuit. It is also observed that parameters selected for vibration reduction of building can effectively achieve large-scale energy harvesting at same time.

Commentary by Dr. Valentin Fuster
2014;():V002T32A002. doi:10.1115/DSCC2014-6185.

This paper considers the optimization of discrete-time control systems for power generation from stochastically-excited linear dynamical systems, characterized by infinite-dimensional dynamics. Weiner-Hopf theory is used to determine the physical upper bound on stationary power generation. The resultant expressions for power generation do not require a finite-dimensional state-space model to be specified at any point in the analysis. However, the constraint that the controller be causal leads to the need to perform spectral factorizations on two associated power spectra. In many cases, analytical solutions to these factorizations are intractable, and approximate numerical techniques must be used. This paper makes use of subspace identification techniques for approximate spectral factorization. The concepts are illustrated in a wave energy conversion example.

Commentary by Dr. Valentin Fuster
2014;():V002T32A003. doi:10.1115/DSCC2014-6247.

In-stream hydrokinetic electricity production, electricity generation from moving currents without the use of dams, has significant potential for increasing electric power production. This project evaluates a control system designed to regulate rotor rate (rpm) to improve power production from in-stream hydrokinetic turbines. The control algorithm is evaluated using both a numerical model of a rigidly mounted tidal turbine and a numerical model of a moored ocean current turbine system. These two system models are each coupled to an induction electric machine model. Based on the turbine torque-speed characteristic, as well as the asynchronous machine features, a Look-Up-Table (LUT) is used to generate the frequency of the sinusoidal voltages of the three phases to be supplied to the machine. However, to compensate for disturbances and perturbations of the power-plant a PI controller is generating a correction term for electrical frequency superimposed to the output of the LUT. A first round of simulations using the numerical models was performed in order to evaluate the developed algorithms.

Commentary by Dr. Valentin Fuster
2014;():V002T32A004. doi:10.1115/DSCC2014-6292.

A two-body self-reacting point absorber is proposed in this paper. This two body system is designed as a floating buoy and a bottom sphere. The energy is harvested through the relative motion between these two bodies. A mechanical motion rectifier (MMR) is used as the power takeoff (PTO) system. The PTO system will experience engagement and disengagement under wave excitation. Due to this nonlinearity, a time domain model is developed. The influences of the PTO parameters such as the equivalent inertia mass and equivalent damping on the absorbed power are obtained in regular waves. The system parameters were chosen to maximize the absorbed power. The performance of this device in irregular waves was also investigated.

Commentary by Dr. Valentin Fuster
2014;():V002T32A005. doi:10.1115/DSCC2014-6317.

A novel electromagnetic pendulum energy harvester with motion regulator is proposed and investigated in this paper. The motion regulator is a mechanical design which can mechanically rectify the irregular bidirectional swing motion of the pendulum into unidirectional rotational motion of the motor, so that no electrical rectifier is needed and voltage drops from diodes can be avoided. Therefore, the energy efficiency can be significantly improved. The working principle of this pendulum mechanical motion regulator is that when the input velocity of the motion regulator is smaller than the output velocity of the motion regulator, the output transmission shaft will disengage from the motion regulator. This disengage mechanism of the pendulum energy harvester will lead to a broad bandwidth frequency response because of the inclination of maximizing the velocity output from the system. The dynamic modeling of the switched linear system (either engaged or disengaged) is established. The simulation results illustrate the power generation performance of the pendulum energy harvester, and the comparison with the traditional energy harvester without motion regulator shows the broadband advantage of the proposed energy harvester. Experiment verification has also been carried out. The experiment results verified that at some frequencies over the natural frequency, the efficiency is increased.

Commentary by Dr. Valentin Fuster

Modeling and Control for Thermo-Fluid Applications

2014;():V002T33A001. doi:10.1115/DSCC2014-5888.

This study presents an intersection of two seemingly separate areas of research frontiers, “prediction and control of thermoacoustic instability” and “stability analysis of neutral-class linear-time-invariant (LTI) and time-delayed systems (TDS)”. The former is a coveted capability which has been elusive to the scientific community over 1½ centuries. Analytical capabilities have been limited due to the complex physics invoking the “combustion” phenomenon. Most available results rely on accumulated empirical knowledge. In this paper we consider a benchmark combustion test platform, which is known as Rijke’s tube. Its representation is simplified to an LTI neutral TDS, stability of which is assessed using a recent mathematical paradigm called the Cluster Treatment of Characteristic Roots (CTCR). CTCR provides a unique non-conservative and exhaustive stability declaration for a Rijke’s tube within the space of its parameters, naturally, under some simplifying assumptions. For those operating conditions which induce instability, we also propose a conventional and simple control strategy which can recover stability. This method is also analyzed using the CTCR paradigm for the first time.

Commentary by Dr. Valentin Fuster
2014;():V002T33A002. doi:10.1115/DSCC2014-6136.

In this paper, we introduce the three bead achiral microswimmers controlled wirelessly using magnetic fields with the ability to swim in bulk fluid. The achirality of the microswimmer introduces unknown handedness of the microswimmer. Here, we propose to use a combination of rotating and static magnetic fields to eliminate the uncertainty in swimming direction. Our experimental results demonstrated excellent capability of direction control as well as agile movements. From the experimentally collected data, we estimated a control-oriented two-wheeled robot model. Finally, we design feedback control for microswimmers based on the estimated kinematic model. In particular, we show that the feedback control law moves the microswimmer from any initial conditions to a target set of microswimmer’s position and angle.

Commentary by Dr. Valentin Fuster
2014;():V002T33A003. doi:10.1115/DSCC2014-6206.

Pneumatic systems possess inherent compliance and potentially variable stiffness that make them an appealing actuator choice for tracking applications where contact and interaction are likely. However, good control of pneumatic systems is impeded by discontinuous and nonlinear dynamics, especially compliance and friction. The most successful previous solutions have either applied high-gain PD or sliding mode control. These achieve tracking control for compliant systems by transforming them into stiffer ones. Model predictive control can better balance precision tracking with compliance (low output impedance), so that the system is safer in case of collision disturbance. It can be coupled with a predictive observer that estimates friction as a known disturbance. The estimate is incorporated into the optimization, improving friction compensation for pneumatics, which has slow dynamics that do not react quickly enough with traditional feedforward compensation. Finally, predictive control enables constrained finite-time optimization, driving the system closer to its peak performance capability.

Commentary by Dr. Valentin Fuster
2014;():V002T33A004. doi:10.1115/DSCC2014-6311.

In this paper, a governing equation for web tension considering thermal and viscoelastic effects in a multi-span system is developed. The thermal effect is included in the web tension dynamics by considering the thermal strain induced by temperature distribution in the web span. The viscoelastic effect is introduced by using a standard linear solid (SLS) model from which a relationship between the applied stress and the resulting strain is obtained. Elevated temperature creep and stress relaxation experiments are conducted on several web materials used in an industrial web processing line to determine the viscoelastic parameters of the utilized viscoelastic model. Model simulations are conducted with the same control systems as those used in an industrial web processing line. Data from model simulations are compared with measured data obtained from the industrial web processing line.

Commentary by Dr. Valentin Fuster

Modeling and Control of IC Engines

2014;():V002T34A001. doi:10.1115/DSCC2014-5975.

This paper presents a novel dual-clutch, transmission-less hybrid electric powertrain architecture using Dual Mechanical Port Machine (DMPM). The proposed architecture offers the vehicle to operate in four different modes i.e. charge depleting mode, charge sustaining mode, highway mode and braking mode. Power/energy flow in each operating mode is explored and operating characteristics of each prime mover in all four modes are analyzed. A detailed system level modeling is conducted for the proposed powertrain. The power split HEV offers a variety of control problems to insure its optimal performance. In this paper, a rule based control has been designed to meet some of the outlined control objectives. The simulation results show that the proposed HEV architecture with a rule based energy management scheme offers high fuel efficiency as compared to its conventional counterpart.

Commentary by Dr. Valentin Fuster
2014;():V002T34A002. doi:10.1115/DSCC2014-6001.

Lithium-Ion (Li-ion) batteries are widely used in electric and hybrid electric vehicles for energy storage. However, a Li-ion battery’s lifespan and performance is reduced if it’s overheated during operation. To maintain the battery’s temperature below established thresholds, the heat generated during charge/discharge must be removed and this requires an effective cooling system. This paper introduces a battery thermal management system (BTMS) based on a dynamic thermal-electric model of a cylindrical battery. The heat generation rate estimated by this model helps to actively control the air mass flow rate. A nonlinear back-stepping controller and a linear optimal controller are developed to identify the ideal cooling air temperature which stabilizes the battery core temperature. The simulation of two different operating scenarios and three control strategies has been conducted. Simulation results indicate that the proposed controllers can stabilize the battery core temperature with peak tracking errors smaller than 2.4°C by regulating the cooling air temperature and mass flow rate. Overall the controllers developed for the battery thermal management system show improvements in both temperature tracking and cooling system power conservation, in comparison to the classical controller. The next step in this study is to integrate these elements into a holistic cooling configuration with AC system compressor control to minimize the cooling power consumption.

Commentary by Dr. Valentin Fuster
2014;():V002T34A003. doi:10.1115/DSCC2014-6146.

Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultra-low levels of engine-out NOx emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper a methodology is introduced, investigating the fuel economy of such a multimode combustion concept in combination with a three-way catalytic converter (TWC). The TWC needs to exhibit unoccupied oxygen storage sites in order to show acceptable performance. But the lean exhaust gas during HCCI operation fills the oxygen storage and leads to a drop in NOx conversion efficiency. Eventually the levels of NOx become unacceptable and a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage. The resulting lean-rich cycling leads to a penalty in fuel economy. In order to evaluate the impact of those penalties on fuel economy, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI and back. Fuel and emissions maps acquired in steady state experiments are used. Two depletion strategies are compared in terms of their influence on drive cycle fuel economy and NOx emissions.

Commentary by Dr. Valentin Fuster
2014;():V002T34A004. doi:10.1115/DSCC2014-6256.

Previously, the authors have designed and implemented an active motion control “virtual crankshaft” for a free piston engine, which enables precise piston tracking of desired trajectories. With this mechanism, the volume of the combustion chamber can be regulated, and therefore the pressure, temperature and species concentrations of in-cylinder gas can be adjusted in real-time which affect the combustion process directly. This new degree of freedom enables us to conduct trajectory-based combustion control. In this paper, a model of the free piston engine running homogeneous charge compression ignition combustion under variant piston trajectories is presented. The variant piston trajectories have the ability to change the compression ratio and accommodate different piston motion patterns between the top dead center and the bottom dead center. The Lawrence Livermore National Laboratory reduced n-heptane reaction mechanism is employed in the model in order to describe the chemical kinetics under various piston trajectories. Analysis of the simulation results is then presented which reveals the piston trajectory effects on the combustion phenomena in terms of in-cylinder gas temperature trace, indicated output work, heat loss and radical species accumulation process.

Commentary by Dr. Valentin Fuster
2014;():V002T34A005. doi:10.1115/DSCC2014-6275.

A low-order homogeneous charge compression ignition (HCCI) combustion model to support model-based control development for spark ignition (SI)/HCCI mode transitions is presented. Emphasis is placed on mode transition strategies wherein SI combustion is abruptly switched to recompression HCCI combustion through a change of the cam lift and opening of the throttle, as is often employed in studies utilizing two-stage cam switching devices. The model is parameterized to a steady-state dataset which considers throttled operation and significant air-fuel ratio variation, which are pertinent conditions to two-stage cam switching mode transition strategies. Inspection and simulation of transient SI to HCCI (SI-HCCI) mode transition data shows that the extreme conditions present when switching from SI to HCCI can cause significant prediction error in the combustion performance outputs even with the model’s adequate steady-state fit. When a correction factor related to residual gas temperature is introduced to account for these extreme conditions, it is shown that the model reproduces transient performance output time histories in SI-HCCI mode transition data. The model is thus able to capture steady-state data as well as transient SI-HCCI mode transition data while maintaining a low-order cycle to cycle structure, making it tractable for model-based control of SI-HCCI mode transitions.

Commentary by Dr. Valentin Fuster
2014;():V002T34A006. doi:10.1115/DSCC2014-6283.

Energy management strategies in a parallel Hybrid Electric Vehicle (HEV) greatly depend on the accuracy of internal combustion engine (ICE) data. It is a common practice to rely on static maps for required engine torque-fuel efficiency data. The engine dynamics are ignored in these static maps and it is uncertain how neglecting these dynamics can affect fuel economy of a parallel HEV. This paper presents the impact of ICE dynamics on the performance of the torque split management strategy. A parallel HEV torque split strategy is developed using a method of model predictive control. The control strategy is implemented on a HEV model with an experimentally validated, dynamic ICE model. Simulation results show that the ICE dynamics can degrade performance of the HEV control strategy during the transient periods of the vehicle operation by more than 20% for city driving conditions in a common North American drive cycle. This also leads to substantial fuel penalty which is often overlooked in conventional HEV energy management strategies.

Commentary by Dr. Valentin Fuster

Modeling and Control of Manufacturing Processes

2014;():V002T35A001. doi:10.1115/DSCC2014-5914.

Ink-jet 3D printing is a promising technology for additive manufacturing, with the potential for impacting a wide variety of industries. In traditional ink-jet 3D printing, the part is built up by depositing droplets layer upon layer in an open-loop manner. Droplet and edge dimensions are typically predicted experimentally and are assumed to remain constant through the printing process. However, there is no guarantee of consistent droplet shape and dimensions or the smoothness of the finished parts due to uncertainties in the manufacturing process. To address this issue, we propose a model-based feedback control law for ink-jet 3D printing that uses a height sensor for measuring profile height after each layer for determining the appropriate layer patterns for subsequent layers. Towards this goal, a simple model describing the relationship between profile height change and droplet deposition in the layer building process is first proposed and experimentally identified. Based on this model, a closed-loop layer-to-layer control algorithm is then developed for the ink-jet printing process. Specifically, the proposed algorithm uses a model prediction control algorithm to minimize the difference between the predicted height and the desired height and the predicted surface unevenness after a fixed number of layers. Experimental results show that the algorithm is able to achieve more consistent shapes between layers, reduced edge shrinking of the part, and smoother surface of the top layer.

Commentary by Dr. Valentin Fuster
2014;():V002T35A002. doi:10.1115/DSCC2014-5930.

A high-speed milling system is considered, which is prone to chatter vibration, a stability condition dependent on system parameters (e.g., cutting force coefficients). This work is motivated by the need for model parameters which can be used in stability analysis. An Extended Kalman Filter (EKF) is proposed to estimate cutting force coefficients for each tooth in a low-radial-immersion milling process to aid chatter stability prediction. The proposed EKF utilizes tool deflection measurements and no force measurements. The model used in the EKF is found to be observable, a quality required to achieve valid state estimations. Running the EKF with experimental tool deflection measurements produces estimates of cutting force coefficients that result in good correlation between simulation (using the estimated coefficients) and experiment. Such an EKF may help customize chatter stability analysis to any particular tool-workpiece system.

Topics: Cutting , Milling
Commentary by Dr. Valentin Fuster
2014;():V002T35A003. doi:10.1115/DSCC2014-6110.

Electrohydrodynamic jet (e-jet) printing is a recent micro-manufacturing technique that uses electrostatic force to draw out ink from a conductive nozzle onto a conductive substrate. While the advantages (high speed and resolution, flexibility) of e-jet printing over ink jet printing and other microfabrication methods are abundant, precise control of the process is necessary for successful commercialization of the technology. This paper shows how visual feedback through image processing may be used to regulate the volume of printed droplets for increased manufacturing precision.

Commentary by Dr. Valentin Fuster
2014;():V002T35A004. doi:10.1115/DSCC2014-6173.

The Laser Metal Deposition (LMD) process is an additive manufacturing process in which a laser and a powdered material source are used to build functional metal parts in a layer by layer fashion. While the process is usually modeled by purely temporal dynamic models, the process is more aptly described as a repetitive process with two sets of dynamic processes: one that evolves in position within the layer and one that evolves in part layer. Therefore, to properly control the LMD process, it is advantageous to use a model of the LMD process that captures the dominant two dimensional phenomena and to address the two-dimensionality in process control. Using an identified spatial-domain Hammerstein model of the LMD process, the open loop process stability is examined. Then, a stabilizing controller is designed using error feedback in the layer domain.

Commentary by Dr. Valentin Fuster
2014;():V002T35A005. doi:10.1115/DSCC2014-6178.

Remelting is used in the production of superalloy ingots. In these processes, stabilization of the solidification front is crucial in the prevention of segregation defects. However, models that account for solidification dynamics often are distributed-parameter multi-physics models that are not used in process control due to their complexity. This paper outlines model reduction for a remelting process based on a multi-physics finite volume model. A reduced-order model is constructed from a state-space realization where only transport phenomena are included. Balancing-free square-root singular perturbation approximation is used to construct a minimal reduced system, and then modal residualization is performed to remove modes that lie outside of the bandwidth of the actuators. The obtained reduced-order model was used to design an LQG controller. Simulation results verify that using the proposed reduced-order model for estimation and control can result in more accurate solidification control, when compared to a simplified model that accounts only for thermal processes.

Topics: Solidification
Commentary by Dr. Valentin Fuster

Modeling and Estimation of Alternative Propulsion Systems

2014;():V002T36A001. doi:10.1115/DSCC2014-5820.

In this paper a conditional Extended Kalman Filter is applied to battery model parameter and state estimations. A decision logic, based on battery input and output data, is designed such that parameter update is stopped when persistent excitation conditions are not met. Persistent excitation conditions are represented by a simpler, easier to implement set of calibrations. Examples, both from desktop simulation, and real world vehicle testing, have been provided to support the validity of this algorithm. The proposed strategy has been successfully deployed in production FHEV/PHEV vehicles.

Commentary by Dr. Valentin Fuster
2014;():V002T36A002. doi:10.1115/DSCC2014-5825.

Prediction of battery system responses and capability for next few seconds can provide key information to use battery hardware effectively. The prediction performance will be much improved, when battery models can capture the real battery responses as accurate as possible. Equivalent circuit models (ECMs) have been used for control purpose due to their proper balance between computational efficiency and prediction accuracy. The limitations of ECMs can be efficiently compensated through real-time model parameter estimation. Further enhancement is possible by improving system observability and robustness, specifically effective under low temperature and aggressive driving. This paper proposes an approach to improve the robustness and accuracy in estimating parameters by reformulating ECMs with new parameters. The proposed approach can estimate battery parameters less sensitive to both external disturbance and possible model mismatch under various driving conditions.

Commentary by Dr. Valentin Fuster
2014;():V002T36A003. doi:10.1115/DSCC2014-5937.

Rising fuel prices and fast depletion of energy resources have led to an increasing interest in green technology. Hybrid vehicles due to their ability to draw power from multiple energy sources are a preferred option. Although the components such as supercapacitors, batteries, internal combustion engine, or a fuel cell, generally needed to realize a hybrid topology are known, the topology of their arrangement and realization by interchanging the components and combining different power sources, needs to be studied. In this paper, various hybrid powertrain configurations in combination with different storage elements are compared and a suitable control and power management strategy for a multi-power source powertrain system is implemented using a scalable test rig. In order to validate the component design and topology, Hardware-in-the-Loop (HiL) tests are carried out with the help of an emulator set-up.

Commentary by Dr. Valentin Fuster
2014;():V002T36A004. doi:10.1115/DSCC2014-5999.

For hybrid electric vehicles (HEVs), especially for diesel-electric hybrid vehicles, the low exhaust gas temperature induced by the hybridization and fuel economy optimization will bring significant impact on the performance of the exhaust gas aftertreatment systems, and may consequently lead to violation of the tailpipe emission constraints. To investigate the influence of diesel powertrain hybridization on the aftertreatment system and tailpipe emissions, an integrated HEV model is established by incorporating the thermodynamics models of the aftertreatment systems. This comprehensive model is able to predict engine-out nitrogen oxides (NOx) concentration, exhaust gas temperature, and to describe the temperature dynamics in the aftertreatment systems. A static map of selective catalytic reduction (SCR) system temperature-dependent de-NOx efficiency is utilized, so that the tailpipe NOx can be predicted. To investigate the tradeoff between fuel consumption and emissions for diesel HEV with aftertreatment systems, a preliminary study is carried out on optimally balancing both aspects via a model predictive control scheme. This controller is designed with an explicit consideration of HEV tailpipe NOx emission constraint. The simulation results show that the HEV tailpipe NOx emissions can be regulated by slightly sacrificing the fuel economy.

Commentary by Dr. Valentin Fuster
2014;():V002T36A005. doi:10.1115/DSCC2014-6101.

This paper presents a systematic methodology based on structural analysis and sequential residual generators to design a Fault Detection and Isolation (FDI) scheme for nonlinear battery systems. The faults to be diagnosed are highlighted using a detailed hazard analysis conducted for battery systems. The developed methodology includes four steps: candidate residual generators generation, residual generators selection, diagnostic test construction and fault isolation. State transformation is employed to make the residuals realizable. The simulation results show that the proposed FDI scheme successfully detects and isolates the faults injected in the battery cell with cooling system at different times. In addition, there are no false or missed detections of the faults.

Commentary by Dr. Valentin Fuster
2014;():V002T36A006. doi:10.1115/DSCC2014-6116.

Electric and hybrid electric vehicles (EV/HEVs) have attracted considerable interest among automobile manufactures worldwide due to their advantages of better fuel economy. To guarantee safe, clean and reliable operation of electric drive systems, it is imperative to develop reliable and robust diagnostic schemes so that appropriate corrective actions can be taken in case a component or subsystem fail to operate normally. This paper proposes a diagnostic scheme for permanent magnet synchronous motor (PMSM) drives in EV/HEV applications. The proposed strategy uses two generalized observers to detect and isolate current, speed, and rotor position sensor faults in a PMSM drive. Since in real driving cases, the load torque is usually unknown due to unexpected road disturbance, the proposed diagnostic scheme uses an Unknown Input Observer (UIO) to estimate the load torque. The diagnostic algorithm is validated in Matlab/Simulink using the Ohio State University EcoCAR as testbed. The simulation results show that the proposed scheme is effective in detecting and isolating various sensor faults under road disturbance.

Commentary by Dr. Valentin Fuster

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