ASME Conference Presenter Attendance Policy and Archival Proceedings

2017;():V002T00A001. doi:10.1115/DSCC2017-NS2.

This online compilation of papers from the ASME 2017 Dynamic Systems and Control Conference (DSCC2017) 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presenter attendance policy, if a paper is not presented at the Conference by an author of the paper, the paper will not be published in the official archival Proceedings, which are registered with the Library of Congress and are submitted for abstracting and indexing. The paper also will not be published in The ASME Digital Collection and may not be cited as a published paper.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


2017;():V002T01A001. doi:10.1115/DSCC2017-5164.

An analytical method is used to develop a model to calculate steady-state eddy-current damping effects in two configurations of magnetic levitation (maglev) systems. The eddy-current based force (eddy-current force) is used for high precision positioning of a levitated permanent magnet in maglev systems. In these systems, the motion of the levitated permanent magnet and changes of the coil’s currents, generate eddy current in the conductors. The proposed analytical model is used to calculate both effects. A conductive cylindrical shell around the levitated object is implemented as a new technique to generate eddy currents in maglev systems. The model is also employed to obtain eddy-current damping effects in a system with a conductive plate beneath the levitated object. The analytical models match results from high fidelity finite element analysis (FEA) with acceptable accuracy in a wide range of operations. Advantages of the two configurations are discussed.

Commentary by Dr. Valentin Fuster
2017;():V002T01A002. doi:10.1115/DSCC2017-5188.

In response to current trends in robotic systems, particularly those relevant to the area of actuators for use in rovers and space manipulators, researchers at Northeastern University have developed a precision prototype of a novel compact Brushless AC/gear-bearing actuator system in collaboration with the National Aeronautics and Space Administration’s Jet Propulsion Laboratory. As part of the preliminary investigations into the functional behavior and potential applications for this device, efforts have been made toward characterizing the open-loop behavior and realizing a control system to provide reliable closed-loop performance. To date, a preliminary open-loop model of the prototype has been developed and several active nonlinearities have been identified. As a first approach toward the closed-loop control of this system, a simplified linear model of the open-loop plant has been adopted which regards all nonlinearities and unidentified sources of error as unknown disturbances and parameter uncertainties. Under this paradigm, there is a clear need for a robust adaptive control scheme to handle both the resulting uncertainties and potential for variations during warm-up, break-in, and regular operational phases. The control technique known as Sliding Mode Control with Perturbation Estimation (SMCPE) is well suited to meet these needs, and is further capable of rejecting the ‘disturbances’ resulting from the model simplification. In this work, the nonlinear and simplified open-loop models are presented, as well as the relevant control theory and SMCPE controller design. Utilizing set-point regulation as a motivating example, results from numerical simulations and real-time control experiments are presented from which it is concluded that SMCPE provides both the desired robustness and disturbance rejection properties required to achieve satisfactory operation of the gear-bearing actuator in a closed-loop speed control mode.

Commentary by Dr. Valentin Fuster
2017;():V002T01A003. doi:10.1115/DSCC2017-5204.

This paper presents a complete experimental implementation of an Extended High-Gain Observer (EHGO) based disturbance and uncertainty estimator for use in quadrotor control. The system is designed as a multi-time-scale system to deal with mechanical underactuation and to ensure convergence of EHGO estimates for use in the output feedback control. The lumped, estimated disturbance is passed into the rotational dynamic inversion based control, and the feedback linearization based translational control to cancel the estimated disturbances. This results in a feedback control scheme that is robust to external disturbances as well as model uncertainties, such as an uncertain air-frame mass and rotational inertial matrix. The control is verified through simulation and experimental results.

Topics: Uncertainty
Commentary by Dr. Valentin Fuster
2017;():V002T01A004. doi:10.1115/DSCC2017-5269.

Self-balancing human transporters are naturally unstable. However, when coupled with sophisticated control laws, these machines can provide mobility within a finite stability envelope. Challenging environmental conditions, or unanticipated operator action, can cause these machines to exhibit unexpected behavior. In an effort to better understand the behavior of these systems inside and outside the stability envelope, a dynamic model of a hoverboard is presented. Motion-capture data is also presented in which an operator’s interactions with the hoverboard were recorded.

Commentary by Dr. Valentin Fuster

Estimation and Identification

2017;():V002T04A001. doi:10.1115/DSCC2017-5075.

Modern control systems heavily relay on sensors for closed-loop feedback control. Degradation of sensor performance due to sensor aging affects the closed-loop system performance, reliability, and stability. Sensor aging characterized by the sensor measurement noise covariance. This paper proposes an algorithm used to identify the slow varying sensor noise covariance online based on system sensor measurements. The covariance-matching technique, along with the adaptive Kalman filter is utilized based on the information about the quality of weighted innovation sequence to estimate the slow time-varying sensor noise covariance. The sequential manner of the proposed algorithm leads to significant reduction of the computational load. The covariance-matching of the weighted innovation sequence improves the prediction accuracy and reduces the computational load, which makes it suitable for online applications. Simulation results show that the proposed algorithm is capable of estimating the slow time-varying sensor noise covariance for MIMO systems with white noise whose covariance varies linearly, exponentially, or linearly with added sinusoid perturbation. Furthermore, the proposed estimation algorithm shows a reasonable convergence rate.

Commentary by Dr. Valentin Fuster
2017;():V002T04A002. doi:10.1115/DSCC2017-5097.

An active suspension based on Linear Quadratic Gaussian (LQG) optimal controller is an effective system for enhancing the ride comfort and handling characteristics of a vehicle. LQG requires a good plant model for success, and this may be difficult to extract using a single inertial measurement device in the presence of noise. This paper presents a method for estimating the vehicle states by measuring both the vehicle bounce and pitch accelerations using an Inertial Measurement Unit (IMU) with position uncertainty relative to the sprung mass center of gravity. Frequency domain methods are used for System Identification (SysId). The state estimation is based on channel-by-channel model estimation using uncorrelated random excitation which is applied to the front wheels, rear wheels, front actuator, and rear actuator. An anti-aliasing filter eliminates false response harmonics and a Kalman filter is used to estimate the current states of the actual plant and the LQR block for the full-states-feedback controller. The controllers and observer are implemented in simulation using a four degree-of-freedom half car linear model.

Commentary by Dr. Valentin Fuster
2017;():V002T04A003. doi:10.1115/DSCC2017-5180.

This paper presents the modeling, identification, and compensation of cogging and resistive forces in tubular permanent magnet linear motors (PMLMs). An observer-based model that includes the effects of cogging, backlash, inertia, Coulomb, and viscous frictions, as well as back electromotive force is presented. Least square optimization is carried out to identify the model, and the weakness of fast Fourier transform (FFT) in PMLMs with short translators for precise wavelength determination is discussed. The identified model is used as a directional estimator in a closed-loop controller to compensate the disturbance forces induced by the PMLM to a secondary mechanism. Experimental results show a reduction of the disturbance force energy up to 31.96 dB.

Commentary by Dr. Valentin Fuster
2017;():V002T04A004. doi:10.1115/DSCC2017-5236.

Recent advances in flexible and wireless sensors, soft materials, and additive manufacturing, have stimulated demands for developing intelligent systems that can achieve multidisciplinary objectives (e.g., mechanical strength, thermal conductivity, state and input estimation, controllability, and others). Existing studies often decouple these objectives through sub-system level design, e.g., topology and material design for mechanical and thermal properties, and filter and sensor/actuator design for observability and controllability, assuming that the sub-systems have minimal influences to each others. To investigate the validity of this assumption, we take a unique angle at studying how the topology of the system influences both structural performance (e.g., compliance under static loads) and input observability (e.g., the error in estimating the loads). We reveal a tradeoff between these two objectives and derive the Pareto frontier with respect to the topology. This preliminary result suggests the necessity of a multiobjective formulation for designing intelligent structures, when significant tradeoffs among system objectives exist.

Commentary by Dr. Valentin Fuster
2017;():V002T04A005. doi:10.1115/DSCC2017-5259.

Condition monitoring and fault diagnosis of induction motor play a critical role in operation safety and production efficiency. In recent study, sparse representation has demonstrated its simplicity in training, robustness to noise and high accuracy in classification. This paper evaluates the effectiveness of sparse representation as an alternative approach to induction motor fault diagnosis with fault classification rate and robustness to noise as performance measure. Aiming at eliminating the human intervention in fault characteristic frequency detection and extensive feature extraction steps in traditional method, the spatial pattern of the vibration signal is studied as the classifier input. The residual sparsity index (RSI) is proposed to quantify the degree of multi-class data separation and evaluate the reliability of classification results. Experimental results show that the sparse representation method using vibration signal achieves high motor multi-fault classification accuracy and good robustness to noise, with no human intervention required for fault characteristic pattern detection and the need for long feature extraction eliminated. Finally, RSI confirms the high overall reliability of classification results.

Commentary by Dr. Valentin Fuster
2017;():V002T04A006. doi:10.1115/DSCC2017-5273.

This paper presents a methodology for estimating the relative position between a micro aerial vehicle (MAV) and a base-station using a monocular camera and an ultra-wide band (UWB) ranging module. The pinhole camera model is used to derive a relationship that relates the 3D position of the vehicle to its location in the image frame and the estimated range. A tracking algorithm was implemented to track the location of a light emitting diode (LED) array mounted to the MAV with a low cost webcam. The range was determined by using a two-way ranging algorithm with the UWB modules. The presented methodology is applied to experimental data collected from a quad-rotor. The results are compared to a motion capture system, and it is shown that this method is able to track the position of the MAV to within the range accuracy of the UWB modules. This relative position estimation methodology has the potential to be a viable component of a navigation system for cooperative vehicles.

Commentary by Dr. Valentin Fuster
2017;():V002T04A007. doi:10.1115/DSCC2017-5291.

In this paper, machine learning methods are proposed for human intention estimation based on the change of force distribution on the interaction surface during human-robot collaboration (HRC). The force distribution under different human intentions are examined when the human and robot are jointly carrying the same piece of object. A pair of Robotiq tactile sensors is applied to monitor the change of force distribution on the interaction surface. Three machine learning algorithms are tested on recognition of human intentions based on the force distribution patterns on the contact surface of grippers for the manipulator. The K-nearest Neighbor model is selected to build a real-time framework, which includes human intention estimation and cooperative motion planning for the robot manipulator. A real-time experiment is conducted to validate the method, which suggests the human intention estimation approach can help enhance the efficiency of HRC.

Commentary by Dr. Valentin Fuster
2017;():V002T04A008. doi:10.1115/DSCC2017-5326.

Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in the Kalman filter. To address these combined issues, we propose a robust Kalman-type filter in the presence of non-Gaussian noise that uses information from state constraints. The proposed filter, called the maximum correntropy criterion constrained Kalman filter (MCC-CKF), uses a correntropy metric to quantify not only second-order information but also higher-order moments of the non-Gaussian process and measurement noise, and also enforces constraints on the state estimates. We analytically prove that our newly derived MCC-CKF is an unbiased estimator and has a smaller error covariance than the standard Kalman filter under certain conditions. Simulation results show the superiority of the MCC-CKF compared with other estimators when the system measurement is disturbed by non-Gaussian noise and when the states are constrained.

Topics: Kalman filters
Commentary by Dr. Valentin Fuster
2017;():V002T04A009. doi:10.1115/DSCC2017-5344.

Maintaining Line-Of-Sight (LOS) between the receiver and the transmitter is an inherent challenge associated with light-emitting diode (LED)-based free space optical communication systems, especially when such systems are used by mobile robots. Due to constant movement of underlying robotic platforms and other unwanted disturbances, there is a need for an active alignment system that allows the receiver to constantly track the direction of the transmitting device. In this paper, we propose an active alignment control system, equipped with two degree-of-freedom (DOF) actuation and capable of tracking a transmitting source moving in the three-dimensional (3D) space. A 3D extension of a previously proposed Extended Kalman Filter-based algorithm is used to estimate the components of the angle between the receiver orientation and the receiver-transmitter line, which are used subsequently to adjust the receiver orientation. The algorithm uses only the measured light intensity from a single photo-diode, where successive measurements are obtained via a circular scanning technique. Simulation results are presented to illustrate the proposed approach and explore the tradeoffs in the design of the scanning pattern. In particular, a scheme with adaptively adjusted scanning amplitude is shown to deliver satisfactory alignment performance with actuation effort.

Topics: Filters
Commentary by Dr. Valentin Fuster
2017;():V002T04A010. doi:10.1115/DSCC2017-5347.

Increasing demand for high precision positioning systems has motivated significant research in this field. Within this field, dual-stage nanopositioning systems have the unique potential to offer high-speed and long-range positioning by coupling a short-range, high-speed actuator with a long-range, low-speed actuator. In this paper, design considerations for a spatial filter are presented. The spatial filter allows for control allocation based on range of the signal as opposed to more commonly used frequency-based control allocation. In order to understand the spatial filtering approach more fully, this paper analyzes the filter in detail to understand limitations and give the user a more clear understanding of the approach. Simulation results are included to illustrate aspects of the discussion.

Topics: Design , Filters
Commentary by Dr. Valentin Fuster

Uncertain Systems and Robustness

2017;():V002T05A001. doi:10.1115/DSCC2017-5013.

The dynamics we treat here is a very special and degenerate class of linear time-invariant time-delayed systems (LTI-TDS) with commensurate delays, which exhibit a double imaginary root for a particular value of the delay. The stability behavior of the system within the immediate proximity of this parametric setting which creates the degenerate dynamics is investigated. Several recent investigations also handled this class of systems from the perspective of calculus of variations. We approach the same problem from a different angle, using a recent paradigm called Cluster Treatment of Characteristic Roots (CTCR). We convert one of the parameters in the system into a variable and perturb it around the degenerate point of interest, while simultaneously varying the delay. Clearly, only a particular selection of this arbitrary parameter and the delay enforce the degeneracy. All other adjacent points would be free of the mentioned degeneracy, and therefore can be handled with the CTCR paradigm. Analysis then reveals that the parametrically limiting stability behavior of the dynamics can be extracted by simply using CTCR. The results are shown to be very much aligned with the other investigations on the problem. Simplicity and numerical speed of CTCR may be considered as practical advantages in analyzing such systems. This approach also exhibits the capabilities of CTCR in handling these degenerate cases contrary to the convictions in earlier reports. An example case study is provided to demonstrate these features.

Commentary by Dr. Valentin Fuster
2017;():V002T05A002. doi:10.1115/DSCC2017-5199.

This paper aims at developing a robust gain-scheduled proportional-integral-derivative (PID) control design method for a linear-parameter-varying (LPV) system. It is recognized in the literature that robust fixed-order controller design can be formulated as a feasibility problem of a bilinear matrix inequality (BMI) constraint. Unfortunately, the search for a feasible solution of a BMI constraint is a NP hard problem in general. A common way to solve this dilemma is to apply a linearization method, such as variable change method or congruence transformation, to transform the BMI into LMI. The applicability of the linearization method depends on the specific structure of the problem at hand and cannot be generalized. This paper formulates the gain-scheduled PID controller design as a feasibility problem of a quadratic matrix inequality (QMI) constraint, which covers the BMI constraint as a special case. An augmentation of the newly developed sequential LMI optimization method is proposed to search for a feasible solution of a QMI constraint iteratively. In the application part, a vehicle lateral control problem is presented to demonstrate the applicability of the proposed algorithm to a real-world output feedback control design.

Commentary by Dr. Valentin Fuster
2017;():V002T05A003. doi:10.1115/DSCC2017-5223.

Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides a system with extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration. The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration in order to reach time-changing combat environments and constantly update their techniques. Previous research reveals that life cycle costs, especially acquisition costs, shrink significantly as a result of fleet modularization. In addition, military field demands and enemy attacks are highly unpredictable and uncertain. Hence, it is of interest to the US Army to investigate the robustness and adaptability of a modular fleet operation system against demand uncertainty. We model the fleet operation management in a stochastic state space model while considering time delays from operational actions, as well as use model predictive control (MPC) to attain real-time optimal operation actions based on the received demands and predicted system status.

Analyses on the robustness and adaptability of how a modular vehicle fleet reacts to the demand disturbance and noise have been very limited, although research on operation management and model prediction control have been ongoing for many years. In our current study, we model all the main processes in a fleets operation into an integrated system. These processes include module resupply, vehicle transportation, and on-base assembly, disassembly, reconfiguration (ADR) actions. We also consider the fact that delayed field demands trigger additional demands, which might cause system instability under improper operational strategies. We have designed a predictive control approach that includes an optimizer and a simulation process to monitor and control the fleet operation. Under the identical mission demands and fleet configuration settings, a modular vehicle fleet shows a faster reaction speed than a conventional fleet once demand disturbance and noise are injected. Although our study is inspired by a military application, it is not hard to notice that our system also represents a simplified supply chain structure. Thus, our methodology can also be generalized for civilian applications.

Commentary by Dr. Valentin Fuster
2017;():V002T05A004. doi:10.1115/DSCC2017-5258.

As composite materials are becoming increasingly applied in actively controlled flexible structures, the need for practical uncertainty bounding to capture the effect of normal manufacturing variations on their dynamic behavior is also increasing. Currently, there is a lack of quantification of manufacturing variation of composite materials cast in a robust control framework. This work presents a simple experimental study on a particular case of composite member. The modal parameters of a set of 12 unidirectional carbon fiber reinforce polymer beams are identified. A nominal finite element model is numerically fit to the average experimental natural frequencies and antiresonances. The model is augmented with real parametric uncertainties placed on the modal parameters. The bound on the uncertainties is found both deterministically, to capture all experimentally observed data, and stochastically using a predetermined confidence interval. The two uncertainty bounding approaches are compared through the resulting bound on the beam model frequency response. Also, simulations are conducted to compare possible time responses using the two uncertainty bounds. It is found that the utilized structure of parametric uncertainties is effective at capturing the experimentally observed behavior.

Commentary by Dr. Valentin Fuster
2017;():V002T05A005. doi:10.1115/DSCC2017-5404.

Vibration energy harvesting seeks to exploit the energy of ambient random vibration for power generation, particularly in small scale devices. Piezoelectric transduction is often used as a conversion mechanism in harvesting and the random excitation is typically modeled as a Brownian stochastic process. However, non-Brownian excitations are of potential interest, particularly in the nonequilibrium regime of harvester dynamics. In this work, we investigate the averaged power output of a generic piezoelectric harvester driven by Brownian as well as (non-Brownian) Lévy stable excitations both in the linear and the Duffing regimes. First, a coupled system of stochastic differential equations that model the electromechanical system are presented. Numerical simulation results (based on the Euler-Maruyama scheme) that show the average power output from the system under Brownian and Lévy excitations are presented for the cases where the mechanical degree of freedom behaves as a linear as well as a Duffing oscillator. The results demonstrate that Lévy excitations result in higher expectation values of harvested power. In particular, increasing the noise intensity leads to significant increase in power output in the Levy case when compared with Brownian excitations.

Commentary by Dr. Valentin Fuster

Path Planning and Motion Control

2017;():V002T07A001. doi:10.1115/DSCC2017-5108.

The proportional-integral-derivative (PID) controller is widely used in motion control systems due to its simplicity and effectiveness. To achieve satisfactory performance, the PID parameters must be properly tuned. Although numerous PID tuning methods were investigated in the past, most of them were based on either time-domain or frequency-domain responses, while integration of features in both domains for PID tuning was less addressed. However, many industrial practitioners still found it difficult to compromise multiple conflicting control objectives, such as fast responses, small overshoot and tracking errors, and good robustness, with PID controllers. Moreover, it is desirable to adjust PID parameters online such that plant variations and unexpected disturbances can be compensated for more efficiently. In view of these requirements, this paper proposes an adaptive PID control law that updates its parameters online by minimizing the time-domain tracking errors subject to frequency-domain constraints that are imposed for loop shaping. By combining optimization criteria in both time and frequency domains for online parameter adjustment, the proposed PID controller can achieve good tracking performance with adequate robustness margin. Then the proposed PID law is applied to control an XZ-table driven by AC servo motors. Experimental results show that the tracking performance of the proposed controller is superior to that of a constant-gain PID controller whose parameters were tuned by the commercial Matlab/Simulink PID tuner.

Topics: Motion control
Commentary by Dr. Valentin Fuster
2017;():V002T07A002. doi:10.1115/DSCC2017-5209.

In this paper, we develop a finite state machine based automated highway driving controller. The controller is described by feedback control laws in each state and state transition conditions. We test the controller in a traffic simulator and evaluate its performance based on a metric function. Furthermore, we propose a stochastic gradient based optimization approach to achieve optimal calibration of the developed controller. We expect that this controller can serve as a baseline for automated driving algorithm developments.

Commentary by Dr. Valentin Fuster
2017;():V002T07A003. doi:10.1115/DSCC2017-5285.

Flappy Bird is a mobile game that involves tapping the screen to navigate a bird through a gap between pairs of vertical pipes. When the bird passes through the gap, the score increments by one and the game ends when the bird hits the floor or a pipe. Surprisingly, Flappy Bird is a very difficult game and scores in single digits are not uncommon even after extensive practice. In this paper, we create three controllers to play the game autonomously. The controllers are: (1) a manually tuned controller that flaps the bird based on a vertical set point condition; (2) an optimization-based controller that plans and executes an optimal path between consecutive tubes; (3) a model-based predictive controller (MPC). Our results showed that on average, the optimization-based controller scored highest, followed closely by the MPC, while the manually tuned controller scored the least. A key insight was that choosing a planning horizon slightly beyond consecutive tubes was critical for achieving high scores. The average computation time per iteration for the MPC was half that of optimization-based controller but the worst case time (maximum time) per iteration for the MPC was thrice that of optimization-based controller. The success of the optimization-based controller was due to the intuitive tuning of the terminal position and velocity constraints while for the MPC the important parameters were the prediction and control horizon. The MPC was straightforward to tune compared to the other two controllers. Our conclusion is that MPC provides the best compromise between performance and computation speed without requiring elaborate tuning.

Commentary by Dr. Valentin Fuster
2017;():V002T07A004. doi:10.1115/DSCC2017-5379.

In this work, we analyze approximations of capture sets [1] for a visibility based pursuit-evasion game. In contrast to the capture problem, the pursuer tries to maintain a line-of-sight with the evader in free space in our problem. We extend the concept of U set initially proposed in [2] for holonomic players to the scenario in which the pursuer is holonomic. The problem of computing the U set is reduced to that of computing time-optimal paths for the non-holonomic vehicles to an arbitrary line. We characterize the primitives for time-optimal paths for the Dubin’s vehicle, Reed-shepps car and a Differential Drive robot. Based on these primitives, we construct the optimal paths and provide an algorithm to compute the U set.

Topics: Approximation
Commentary by Dr. Valentin Fuster
2017;():V002T07A005. doi:10.1115/DSCC2017-5391.

This paper presents a geometric gait design and optimization framework for an idealized model of a planar starfish-inspired robot with curvature-controlled soft actuator arms. We describe the range of motion for each arm under the assumption of constant curvature along the length. Two modes of attachment of the ends of the arms to the ground are considered: fixed in position and orientation, and fixed in position but free to rotate. For each mode, we derive mathematical expressions for the local connection relating controlled shape changes to the displacement of the robot’s center. For the rotating case, we additionally model the individual arms as ideal elastica beams and design gaits based on expected buckling behavior for a special case of symmetric (mirrored) curvature inputs via numerical simulations.

Topics: Robots , Actuators , Design
Commentary by Dr. Valentin Fuster
2017;():V002T07A006. doi:10.1115/DSCC2017-5394.

The ability to track a trajectory without significant error is a vital requirement for mobile robots. Numerous methods have been proposed to mitigate tracking error. While these trajectory-tracking methods are efficient for rigid systems, many excite unwanted vibration when applied to flexible systems, leading to tracking error. This paper analyzes a modification of input shaping, which has been primarily used to limit residual vibration for point-to-point motion of flexible systems. Standard input shaping is modified using error-limiting constraints to reduce transient tracking error for the duration of the system’s motion. This method is simulated with trajectory inputs constructed using line segments and Catmull-Rom splines. Error-limiting commands are shown to improve both spatial and temporal tracking performance and can be made robust to modeling errors in natural frequency.

Commentary by Dr. Valentin Fuster

Tracking Control Systems

2017;():V002T12A001. doi:10.1115/DSCC2017-5014.

A novel trajectory tracking, hovering, and yaw motion control is presented for fully nonlinear model of quadrotors based on integral backstepping. The control law is developed by relating the second time derivatives of linear accelerations to thrust force and roll and pitch moments. A separate control law is then derived for yaw motion. The controller is shown to be asymptotically convergent in presence of modeling uncertainties and constant disturbances. Several simulations and experiments are performed with a quadrotor to verify the controller performance.

Commentary by Dr. Valentin Fuster
2017;():V002T12A002. doi:10.1115/DSCC2017-5088.

The tracking performance of a robot manipulator is controlled using nonlinear active disturbance rejection control (ADRC). The proposed method does not require the complete knowledge of the plant’s parameters, and external disturbances since it is based on the rejection and estimation of the unknown internal dynamics and external disturbances. The proposed method is simple and has minimal tuning parameters. The robustness of the proposed method is discussed against parameter uncertainties and disturbances. First, the mathematical model of the manipulator is developed. ADRC theory is explained. The manipulator is represented in ADRC form. ADRC’s tracking performance for the joints and end-effector is compared to the tracking performance of the robust passivity (RP) control. The simulations prove that the proposed control method achieves good tracking performance compared to RP control. It is shown that ADRC has a lower energy consumption compared to RP control by calculating the power in the input signals.

Commentary by Dr. Valentin Fuster
2017;():V002T12A003. doi:10.1115/DSCC2017-5116.

As a precursor to capsize, marginal stability, resulting from incorrect loading conditions and crew negligence, poses a serious danger to ships. Therefore, as a benchmark problem for preventing capsize, the use of an actively controlled pendulum for the stabilization of a marginally stable ship was analyzed. Lyapunov stability criteria and closed loop eigenvalues were used to evaluate the extent to which a proposed pendulum controller could cope with different ship stability conditions. Equations of motion were solved to observe the controller’s performance under different damping conditions. The behavior of the controller yielded the following results: a marginally stable ship can be stabilized, as long as there is no right hand plane zero; energy dissipation is key to the stabilization of a marginally stable ship; the controller must have knowledge of the ship’s stability to prevent controller-induced excitation; and a stabilized tilted ship is more robust to external disturbances than a stabilized upright ship.

Topics: Ships
Commentary by Dr. Valentin Fuster
2017;():V002T12A004. doi:10.1115/DSCC2017-5228.

Most industrial robots are indirect drive robots, which utilize low torque and high speed motors. Indirect drive robots have gear reducers between the motors and links. Due to the flexibility of transmission units, it is difficult to achieve high accuracy for trajectory tracking. In this paper, a neuroadaptive control, which is essentially a neural network (NN) based adaptive back-stepping control approach, is proposed to deal with this problem. The stability of the proposed approach is analysed using Lyapunov stability theory. A data-driven approach is also proposed for the training of the neural network. The effectiveness of the proposed controller is verified by simulation of both single joint and 6-axis industrial robots.

Commentary by Dr. Valentin Fuster
2017;():V002T12A005. doi:10.1115/DSCC2017-5340.

One of the most popular trajectory-tracking controllers used in industry is the PID controller. The PID controller utilizes three types of gains and the tracking error in order to provide a control gain to a system. The PID gains may be tuned manually or using a number of different techniques. Under most operating conditions, only one set of PID gains are used. However, techniques exist to compensate for dynamic systems such as gain scheduling or basic time-varying functions. In this paper, an adaptive PID controller is presented based on Bayesian theory. The interacting multiple model (IMM) method, which utilizes Bayes’ theorem and likelihood functions, is implemented on the PID controller to present an adaptive control strategy. The strategy is applied to a simulated electromechanical system, and the results of the proposed controller are compared with the standard PID method. Future work is also considered.

Commentary by Dr. Valentin Fuster

Multi-Agent and Networked Systems

2017;():V002T14A001. doi:10.1115/DSCC2017-5055.

This paper deals with the leader-following cooperative output regulation problem for heterogeneous multi-agent systems by considering a switched leader dynamics. The switched leader dynamics is composed by multiple linear models and a switching rule governing the switches among them, which is capable of generating more diverse and sophisticated reference signals so as to enhance the multi-agent system’s capability in coping with more complicated coordination tasks. A novel distributed switching control scheme, namely, the smooth switching control strategy, is proposed to achieve cooperative output regulation performance. Distributed switching stability of the overall network is established using multiple Lyapunov functions from the switching control theory. Moreover, under the proposed design framework, the overall cooperative switching output regulation problem can be decomposed into several independent switching stabilization subproblems, and the associated switching control synthesis conditions for each subproblems are formulated as a set of linear matrix inequalities (LMIs) plus linear algebraic equations. As a result, stabilizing switching rules for the leader and distributed switching protocols for the follower agents can be jointly synthesized via semi-definite programming. A numerical example has been used to demonstrate the effectiveness of the proposed approach.

Commentary by Dr. Valentin Fuster
2017;():V002T14A002. doi:10.1115/DSCC2017-5059.

A local state emulator-based adaptive control law is proposed for multiagent systems with agents having linear time-invariant dynamics. Specifically, we present and analyze a distributed adaptive control architecture, where agents achieve system-level goals in the presence of exogenous disturbances. Apart from existing relevant literature that makes specific assumptions on network topologies, agent dynamics, and/or the fraction of agents subjected to disturbances, the proposed approach allows agents to achieve system-level goals — even when all agents are subject to exogenous disturbances. Several numerical examples are provided to demonstrate the efficacy of our approach.

Commentary by Dr. Valentin Fuster
2017;():V002T14A003. doi:10.1115/DSCC2017-5061.

An important research area in sensor networks is the design and analysis of distributed estimation algorithms for dynamic information fusion in the presence of heterogeneity resulting from (i) nonidentical information roles of nodes and (ii) nonidentical modalities of nodes. In particular, (i) implies that both active (i.e., subject to observations of a process of interest) and passive (i.e., subject to no observations) nodes can be present in the sensor network. Furthermore, (ii) implies that active nodes can observe different measurements from a process (e.g., a subset of active nodes can observe position measurements and the rest can observe velocity measurements for a target tracking problem). In this paper, we focus on heterogeneous sensor networks, sensor networks with (i) and (ii), and present a new distributed input and state estimation approach. In addition to the presented theoretical contribution including the stability and performance of the proposed estimation approach, an illustrative numerical example is also given to demonstrate its efficacy.

Commentary by Dr. Valentin Fuster
2017;():V002T14A004. doi:10.1115/DSCC2017-5066.

The contribution of this paper is a distributed control approach for a class of heterogeneous multiagent systems with unknown leader dynamics. Considering fixed and directed communication graph topologies, we establish a global sufficient condition for uniform ultimate boundedness of the output tracking error between the output of each heterogeneous agent and the output of the leader. If, in addition, the output of the leader is constant, we show that uniform ultimate boundedness reduces to asymptotic synchronization. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed control architecture.

Commentary by Dr. Valentin Fuster
2017;():V002T14A005. doi:10.1115/DSCC2017-5118.

This contribution presents a method to estimate environmental boundaries with mobile agents. The agents sample a concentration field of interest at their respective positions and infer a level curve of the unknown field. The presented method is based on support vector machines (SVMs), whereby the concentration level of interest serves as the decision boundary. The field itself does not have to be estimated in order to obtain the level curve which makes the method computationally very appealing. A myopic strategy is developed to pick locations that yield most informative concentration measurements. Cooperative operations of multiple agents are demonstrated by dividing the domain in Voronoi tessellations. Numerical studies demonstrate the feasibility of the method on a real data set of the California coastal area. The exploration strategy is benchmarked against random walk which it clearly outperforms.

Commentary by Dr. Valentin Fuster
2017;():V002T14A006. doi:10.1115/DSCC2017-5133.

We consider a competition between two swarms of aerial vehicles, where multiple intruder vehicles try to approach and then leave an area that multiple guardian vehicles are protecting. Pre-existing swarming strategies for the guardians to maximize the probability of capturing a single intruder are summarized. This work considers the case where multiple intruders approach the protected area sequentially with varied time intervals, to study the impact of intrusion frequency on the probability of capture. In addition, we formulate a payoff function treating the competition as a zero-sum game, and use this function to design strategies for the intruders, i.e., how to optimize the time interval between intrusions. We propose an intrusion strategy and demonstrate its performance with numerical simulations.

Topics: Vehicles
Commentary by Dr. Valentin Fuster
2017;():V002T14A007. doi:10.1115/DSCC2017-5160.

A distributed model predictive controller is developed for the coordination of subsystems in a more electric aircraft, and its performance is compared with the centralized MPC approach. The distributed MPC solution is based on the alternating direction method of multipliers, that allows local subsystem controllers to converge to a system wide optimal solution while enforcing local state and control constraints. Based on simulations, we demonstrate that this distributed approach to coordination of electrical power and engine subsystems has the potential to achieve comparable performance to the centralized solution.

Topics: Aircraft
Commentary by Dr. Valentin Fuster
2017;():V002T14A008. doi:10.1115/DSCC2017-5179.

This paper presents the robustness analysis for an algorithm that solves simultaneous resource allocation and route optimization problem (SARO). These problems appear in the context of multi-hop routing applications in sensor networks, which require placement of multiple resource nodes and determining routes from each sensor location to a common data destination center via these resource nodes. In [1], we proposed an algorithm based on Maximum Entropy Principle that addressed the determination of locations of these resource nodes and the corresponding multi-hop routing problem such that the total communication cost is minimized. Such placement of resource nodes is sensitive to multiple parameters such as sensor locations, destination center location, communication costs between sensor and resource nodes, between resource nodes, and between resource nodes and destination center. This paper studies the sensitivity of the solution from the algorithm to these parameters. This robustness analysis is necessary since some of these parameters are typically not known precisely, the sensitivity analysis helps the network design by identifying the hierarchy in parameters in terms of how they affect the algorithm solution, and therefore also indicate how precisely these parameters need to be estimated. In this direction, we propose a modification of our algorithm to account for the uncertainty in sensor locations; here a probability distribution of sensor locations instead of their precise locations is assumed to be known. We also present and characterize a phase-transition aspect of the algorithm, where the number of distinct locations of resource nodes increase at certain critical values of annealing variable — a parameter in the algorithm. Simulations are provided that corroborate our analysis and instantiate relative sensitivities between different parameters.

Commentary by Dr. Valentin Fuster
2017;():V002T14A009. doi:10.1115/DSCC2017-5229.

Design of robot swarms inspired by self-organization in social insect groups is currently an active research area with a diverse portfolio of potential applications. In this work, the authors propose a control law for efficient area coverage by a robot swarm in a 2D spatial domain, inspired by the unique dynamical characteristics of ant foraging. The novel idea pursued in the effort is that dynamic, adaptive switching between Brownian motion and Lévy flight in the stochastic component of the search increases the efficiency of the search. Influence of different pheromone (the virtual chemotactic agent that drives the foraging) threshold values for switching between Lévy flights and Brownian motion is studied using two performance metrics — area coverage and visit entropy. The results highlight the advantages of the switching strategy for the control framework, particularly in cases when the object of the search is scarce in quantity or getting depleted in real-time.

Commentary by Dr. Valentin Fuster
2017;():V002T14A010. doi:10.1115/DSCC2017-5303.

A group of simple individuals may show ordered, complex behavior through local interactions. This phenomenon is called collective behavior, which has been observed in a vast variety of natural systems such as fish schools or bird flocks. The Vicsek model is a well-established mathematical model to study collective behavior through interaction of individuals with their neighbors in the presence of noise. How noise is modeled can impact the collective behavior of the group. Extrinsic noise captures uncertainty imposed on individuals, such as noise in measurements, while intrinsic noise models uncertainty inherent to individuals, akin to free will. In this paper, the effects of intrinsic and extrinsic noise on characteristics of the transition between order and disorder in the Vicsek model in three dimensions are studied through numerical simulation.

Commentary by Dr. Valentin Fuster
2017;():V002T14A011. doi:10.1115/DSCC2017-5351.

Localization and tracking of a moving target arises in many different contexts and is an important problem in the field of wireless sensor networks. One class of localization schemes exploits the time-difference-of-arrival (TDOA) of a signal emitted by the target and detected by multiple sensors. Much of the existing work on TDOA-based target localization and tracking adopts a centralized approach, where all measurements are sent to a reference agent which produces an estimate of the target’s location. In this work, we propose a fully distributed approach to target localization and tracking by a group of mobile robots. Specifically, we utilize a Networked Extended Kalman Filter (NEKF) to estimate the target’s location in a distributed manner. The target location estimates by individual robots, which are shown to converge to the true value, are then fed into a distributed control law that maintains a specified formation of the robots around the target, which optimizes the estimation accuracy. In order to reduce the energy expenditure of the robots, we further propose a movement control strategy based on the Cramer-Rao bound to balance the trade-off between estimation performance and the total distance traveled by the robots. A numerical example involving robots with unicycle dynamics is provided to illustrate the performance of the proposed approach.

Commentary by Dr. Valentin Fuster
2017;():V002T14A012. doi:10.1115/DSCC2017-5386.

The purpose of this paper is to examine fundamentals of linear control systems and consider vulnerability of the main cyber physical control system features and concepts under malicious attacks, first of all, stability, controllability, and observability, design of feedback loops, design and placement of sensors and controllers. The detailed study is limited to the most important vulnerability issues in time-invariant, unconstrained, deterministic, linear physical systems. Several interesting and motivations examples are provided. We outline also some basic vulnerability studies for time-invariant nonlinear systems.

Topics: Control systems
Commentary by Dr. Valentin Fuster


2017;():V002T16A001. doi:10.1115/DSCC2017-5043.

In the cell phone protective glass manufacturing industry, glass need to be first ground to a desired thickness, which requires human workers to place the glass pieces precisely into the grinder. We propose to use a 6 DOF industrial robot equipped with vision sensors to automate the process by the “pick and place” task. The precision of the placing depends not only on the vision detection, but also on the calibration of the camera and the glass plane. In this paper, a Maximum a Posteriori (MAP) method is proposed to increase the calibration accuracy. A nominal calibration is first obtained with standard method, then it is corrected with observations. Experimental results shows the increased accuracy of placing.

Topics: Glass , Robots , Calibration
Commentary by Dr. Valentin Fuster
2017;():V002T16A002. doi:10.1115/DSCC2017-5147.

3D printing is a diverse field, in particular for biological or bioengineering applications. As a result, research teams working in this area are often multidisciplinary. A (bio) 3D printer in this research environment should balance performance with ease of use in order to enable system adjustments and operation for all machine users from a wide range of disciplines. This work presents results in the development of an easy-to-use fabrication system capable of producing rectilinear bone scaffolds. Common motion control problems, which are barriers to ease of use, are addressed and implemented in a way that researchers outside of the controls field could easily understand. A dynamic model of a 3-stage position system for bone scaffold fabrication is presented. Further, control design for a feedforward plus feedback controller and a user-friendly ILC feedforward compensator is outlined. The ability of the (bio) 3D printer to print bone scaffolds and the effectiveness of the control architecture is demonstrated.

Topics: Manufacturing
Commentary by Dr. Valentin Fuster
2017;():V002T16A003. doi:10.1115/DSCC2017-5217.

The concept of a hardware-in-the-loop experiment for high speed milling is introduced in this paper. The tool-workpiece interaction is virtually implemented in the experiment while the milling machine with the spindle is used as real element. In this paper, the basic components of the experiment are presented, namely, a contactless displacement sensor, a computational algorithm of the cutting force and a contactless electromagnetic actuator are discussed. Experiments on the prototype of the electromagnetic actuator are also shown to illustrate the potential of the concept. A feasibility study of the hardware-in-the-loop experiment is given, where the effect of the time delay included in the experiment is investigated.

Topics: Milling
Commentary by Dr. Valentin Fuster
2017;():V002T16A004. doi:10.1115/DSCC2017-5240.

Obtaining uniform surface finish across large length scales is extremely important in Chemical Mechanical Planarization (CMP). Existing control strategies use results from model simulations to propose open-loop control strategies to reduce the step height on surfaces being polished. In the present work, we propose a strategy to control the surface profile of substrate during CMP process. The evolution of the surface profile is predicted using the state space model of the polishing process. The resulting state space equation is solved and a closed form solution of the surface profile is obtained as a function of time. Based on the solution, we provide a fundamental limitation for the machining process in terms of the extent of planarization that can be achieved for a given material budget.

Commentary by Dr. Valentin Fuster

Intelligent Transportation and Vehicles

2017;():V002T17A001. doi:10.1115/DSCC2017-5058.

For hybrid electric vehicles, there are output shaft torque fluctuations during the working condition switching process, which reduce the driving comfort of the vehicle. Therefore, corresponding control is necessary to eliminate the torque fluctuations. In this paper, for a dual-mode power-split hybrid system, the steady state energy management strategy under the typical power flow in two modes is studied and an operational condition switching control strategy based on engine torque control and motor speed control is proposed for the system characteristics. Meanwhile, the reason for fluctuations on the switching process based on engine torque control is found out to be the too large inertia moment in the coupling power mechanism. Considering the characteristics of fast speed and torque response of the motor, dynamic coordinated control strategy is proposed to eliminate the torque fluctuations and improve the accuracy of the actual torque relative to the target torque for the two models (i.e., the motor torque compensation control strategies). The model of dual-mode hybrid system was built and the simulation results show that the proposed control strategy has a positive effect on eliminating the torque fluctuations and the target torque of the driver can be accurately tracked.

Topics: Engines , Simulation
Commentary by Dr. Valentin Fuster
2017;():V002T17A002. doi:10.1115/DSCC2017-5069.

Powertrain design for power-split hybrid vehicles using planetary gears has been widely researched. As proved, the total number of candidate designs is large due to the diversity of connection ways between devices and planetary gears. In this paper, a new powertrain design approach is developed and applied to power-split hybrid tracked vehicles. The three-step approach that uses constraint-based configuration selection, automated dynamic modeling, drivability screening and fuel economy screening can further reduce computation load and rapidly converge to superior designs. A case study is conducted to identify better designs compared with current designs for hybrid tracked dozers.

Commentary by Dr. Valentin Fuster
2017;():V002T17A003. doi:10.1115/DSCC2017-5081.

In multi-source hybrid electric vehicles (HEVs), more degrees of freedom are introduced to the power management optimization problem. Formulation of the optimal control problem and selection of solution method significantly affect the realtime applicability of the developed strategy. Moreover, elaborate definition of objective cost function and control variables are the essences to approach the global optimal solution in real-time. In this paper, two novel developed concepts are introduced: Adaptive Dynamic Programming (ADP) and Progressive Optimal Search (POS). The problem definition and solution method are structured in each method to yield minimum fuel consumption and suit realtime applicability. The results are comparatively analyzed with respect to previous developed methods of rule-based (RB) and adaptive RB. Experimental application of the developed methods is conducted using a hardware-in-the-loop (HIL) test-rig to validate the control modules. The comparative analysis emphasizes the advantages of each method from the perspectives of trip cost minimization, charge sustenance, and realtime applicability.

Commentary by Dr. Valentin Fuster
2017;():V002T17A004. doi:10.1115/DSCC2017-5155.

This paper presents a new method for road anomaly detection. The existence of road anomalies is determined by the behaviors of vehicles. A special polynomial named Sum-of-Squares (SOS) polynomial is used as a metric to evaluate the normality of vehicle behaviors. The method can process multiple types of sensor measurements. A feature extraction method is used to obtain concise representations of the sensor measurements. These representations, called feature points, are used to calculate the value of the SOS polynomial. Simulation results have been shown to demonstrate that the proposed method can effectively detect different types of road anomalies.

Commentary by Dr. Valentin Fuster
2017;():V002T17A005. doi:10.1115/DSCC2017-5176.

Measuring road profile is mainly used for a road maintenance purpose. Another interesting application of the knowledge on road profile is vehicle localization if the knowledge is available in real-time. A cost effective and implementable approach of measuring or estimating a road profile in a passenger vehicle is estimating the profile using inertial sensors that is readily installed for active safety features. The suggested method is a Kalman filter based disturbance observer without assuming a constant disturbance. By estimating the disturbance of k-1 time step at the k time step, the constant disturbance assumption is not necessary. The required observer structure and dynamics are presented as well as simulation and experimental results.

Topics: Vehicles , Roads
Commentary by Dr. Valentin Fuster
2017;():V002T17A006. doi:10.1115/DSCC2017-5185.

The objective of this paper is to develop an advanced Vision-and-Ranging-aided Inertial Navigation System (VRINS), which combines a Vision-aided Inertial Navigation System (VINS) with Moving Horizon Estimation (MHE) based ranging measurement update. The traditional VINS estimate suffers the error accumulation from the camera observation, which makes the system diverge and fails to track the vehicle trajectory in long-term operation. Hence, a ranging sensor is integrated with VINS in the sequential-sensor-update structure, which allows the filter to operate for longer duration. The ranging measurement update is developed with the MHE, which directly incorporates the system constraints into the optimization process. The VINS is developed with Cubature Multi-State Constraint Kalman Filter (MSCKF), which has 30-dimension filter state, tight constraints of state transition and observability. Those elements need to be considered in the design of MHE optimization. The implementation of MHE is conducted with CASADI library. The proposed VRINS will be validated using KITTI dataset and compared against the VINS.

Topics: Navigation
Commentary by Dr. Valentin Fuster
2017;():V002T17A007. doi:10.1115/DSCC2017-5200.

Control and design optimization of hybrid electric powertrains is necessary to maximize the benefits of novel architectures. Previous studies have proposed multiple optimal and near-optimal control methods, approaches for design optimization, and ways to solve coupled design and control optimization problems for hybrid electric powertrains. This study presents control and design optimization of a novel hybrid electric powertrain architecture to evaluate its performance and potential using physics-based models for the electric machines, the battery and a near-optimal control, namely the equivalent consumption minimization strategy. Design optimization in this paper refers to optimizing the sizes of the powertrain components, i.e. electric machines, battery and final drive. The control and design optimization problem is formulated using nested approach with sequential quadratic programming as design optimization method. Metamodeling is applied to abstract the near-optimal powertrain control model to reduce the computational cost. Fuel economy, sizes of components, and consistency of city and highway fuel economy are reported to evaluate the performance of the powertrain designs. The results suggest an optimal powertrain design and control that grants good performance. The optimal design is shown to be robust and non-sensitive to slight component size changes when evaluated for the near-optimal control.

Topics: Design , Optimization
Commentary by Dr. Valentin Fuster
2017;():V002T17A008. doi:10.1115/DSCC2017-5244.

The need for less fuel consumption urges effective powertrain management optimization in hybrid vehicles. In this study, we consider the real time power optimization problem of a power split hybrid vehicle. Assuming that the power on demand at the driveline can be predicted and known for each driving cycle, the powertrain management and optimization are conducted at the hybrid powertrain system’s level in a computationally efficient fashion. Specifically, we provide an analytical formulation of the powertrain optimization for the hybrid vehicle by using the Pontryagin’s minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations. In order to justify the applicability of the methodology for real-time implementations, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach.

Commentary by Dr. Valentin Fuster
2017;():V002T17A009. doi:10.1115/DSCC2017-5257.

Modeling and simulation of vehicles can be improved by using actual road surface data acquired by Road Surface Measurement Systems. Due to inherent properties of the sensors used, the data acquired is often ridden with outliers. This work addresses the issue of identifying and removing outliers by extending the robust outlier rejection algorithm, Random Sampling and Consensus (RANSAC). Specifically, this work modifies the cost function utilized in RANSAC in such a way that it provides a smooth transition for the classification of points as inliers or outliers. The modified RANSAC algorithm is applied to neighborhoods of data points, which are defined as subsets of points that are close to each other based on a distance metric. Based on the outcome of the modified RANSAC algorithm in each neighborhood, a novel measure for determining the likelihood of a point being an outlier, defined in this work as its exogeny, is developed. The algorithm is tested on a simulated road surface dataset. In the future this novel algorithm will also be tested on real-world road surface datasets to evaluate its performance.

Topics: Algorithms , Pavement
Commentary by Dr. Valentin Fuster
2017;():V002T17A010. doi:10.1115/DSCC2017-5383.

Visual object detection and tracking is a crucial element of Autonomous vehicles. Vision-based sensors are used to detect road entities such as pedestrians and vehicles, and track them in real-time. Various filters such as Kalman Filters and Particle Filters are used in tracking. However, the tracking of these objects is predominantly done on the image plane. The output from the camera is a perspective image of the environment, where the same number of pixels along different directions of the image may not correspond to the same distance in real-world units. Thus, any motion model assigned to the entity would not capture the exact dynamics of the object as the object moves in the 3D world but is being tracked on a perspective view. In this paper, a system is proposed to track the objects on the Inverse Perspective Map, which is a birds eye view of the environment obtained through a homography transformation of the perspective image. Experiments are conducted to show the working of the technique and the results are presented.

Topics: Vehicles
Commentary by Dr. Valentin Fuster

Sensors and Actuators

2017;():V002T18A001. doi:10.1115/DSCC2017-5060.

Inductive power transfer (IPT) remains one of the most common ways to achieve wireless power transfer (WPT), operating on the same electromagnetic principle as electrical transformers but with an air core. IPT has recently been implemented in wireless charging of consumer products such as smartphones and electric vehicles. However, one major challenge with using IPT remains ensuring precise alignment between the transmitting and receiving coils so that maximum power transfer can take place. In this paper, the use of additional sensing coils to detect and correct lateral misalignments in an IPT systems is modeled and tested. The sensing coils exploit magnetic-field symmetry to give a nonlinear measure of misalignment direction and magnitude. Experiments using such sensing coils give a misalignment-sensing resolution of less than 1 mm when applied to a common smartphone wireless charging system. Voltage readings from the sensing coils are used for feedback control of an experimental two-dimensional coil positioner. This system is able to reduce lateral misalignments to less than Display Formula2 mm in real time, allowing for efficient power transfer. The results of this experiment give confidence that similar sensing coils can be used to reduce lateral misalignments in scaled IPT systems, such as electric-vehicle wireless chargers.

Commentary by Dr. Valentin Fuster
2017;():V002T18A002. doi:10.1115/DSCC2017-5085.

A constant flux magnetostrictive impact sensor is presented along with a discussion of prior applications and previous work on modeling of magnetostrictive sensors. A constant flux magnetostrictive impact sensor, which uses a permanent magnet, is modeled and the system in which it operates is overviewed. A detailed analytical model of the operation of the constant flux magnetostrictive impact sensor is developed. Prototype sensors were tested in both dynamic gap and magnetostrictive modes of operation. The model is validated through comparison of modeling and experimental results.

Topics: Sensors , Modeling
Commentary by Dr. Valentin Fuster
2017;():V002T18A003. doi:10.1115/DSCC2017-5322.

Applications of a constant flux magnetostrictive impact sensor as an engine mount energy harvester and side impact sensor are presented and their operation is discussed. An optimization method is developed and appropriate objective functions are created for each application. Optimization results are presented including a power output increase of the engine mount energy harvester of 65% and a decrease in response time of the side impact sensor to impact events while also decreasing sensor output to non-impact events.

Topics: Sensors , Optimization
Commentary by Dr. Valentin Fuster
2017;():V002T18A004. doi:10.1115/DSCC2017-5333.

In this paper, we present a new concept to couple the mechanical vibration dynamic of electrostatic MEMS sensor with its internal electrical circuit resonance. The concept shows great potential to reduce the excitation voltage for electrostatic MEMS. The proposed actuation method aims to activate the mechanical and electrical resonance of the MEMS circuit, simultaneously; to amplify the voltage across the MEMS and increase the MEMS sensitivity to the input electrostatic forces. Moreover, we propose a method to achieve this amplification for any electrostatic MEMS device regardless of the values of its electrical and mechanical resonance frequencies. The proposed concept is studied theoretically and validated experimentally for a commercial MEMS sensor. A voltage amplification of more than 21 times and a MEMS amplitude amplification of over 6 times was observed.

Commentary by Dr. Valentin Fuster

Diagnostics and Detection

2017;():V002T19A001. doi:10.1115/DSCC2017-5138.

The introduction of hybrid vehicle architectures into the mass car market has dramatically increased fault detection and mitigation strategies seen in vehicles to match the growth in potential failures coming from increasingly complex powertrain architectures. To meet this increased demand for fault detection and mitigation of multiple powertrain components, advanced methodologies have been developed to determine the functional safety of systems. This paper focuses on the use of one of those advanced methodologies, structural analysis, to develop the design, implementation, diagnostics, and control of a prototype automated manual transmission. Structural analysis is the concept of analyzing the mathematical structure of a system to determine the diagnostic capabilities of sensors in the system. From this information, a controls strategy can be developed to address potential failure modes of a system utilizing the derived equations and knowledge of which sensors provide coverage for failure modes analyzed. Moreover, the need for additional sensors can be determined through this analysis. Using structural analysis, the Ohio State University EcoCAR 3 research team carried out a diagnostic and mitigation study during the development of their automated manual transmission.

Commentary by Dr. Valentin Fuster
2017;():V002T19A002. doi:10.1115/DSCC2017-5183.

Bearing faults are one of the main reasons for rotary machine failure. Monitoring bearing vibration signals is an effective method for diagnosing faults and preventing catastrophic failures in rotary mechanisms. The state-of-the-art vibration monitoring algorithms are mainly based on frequency or time-frequency domain analysis of rotary machines that are operating in steady state. However, the steady state assumption is not valid in applications where the loads and speeds are time-varying. Finding a method for capturing the variability in vibration signals, which are caused by varying loads and speeds, is still an open research problem with potentially many applications in emerging areas such as electric vehicles. In this paper, we address the problem of vibration signal monitoring by applying a feature extraction algorithm to rotary machine signals measured by accelerometers. The proposed method, which is based on the wavelet scattering transform, achieves overall high accuracy while being computationally affordable for real-time implementation purposes. In order to verify the effectiveness of the proposed methodology, we apply our technique to a well-known vibration benchmark dataset with variable load. Our algorithm can diagnose various faults with different intensities with an average accuracy of 99% and thus effectively outperforming all prior reported work on this dataset.

Commentary by Dr. Valentin Fuster
2017;():V002T19A003. doi:10.1115/DSCC2017-5294.

The intake air filter monitor is a diagnostics/prognostics feature that aims to determine the condition of the intake air filter in the internal combustion engine. This feature is required to indicate the need for maintenance whenever the filter is clogged to the point where the engine performance may be impacted. The prognostics of the air filter helps in saving on operational costs, maintaining the optimal fuel economy, and planning for service/maintenance before failure.

In this paper, one diagnostics/prognostics method is reviewed and a generic statistics based process is proposed to convert the percent life remaining of the air filter into time and distance indicators. The estimated indicators are determined based on the vehicle driving statistics together with the degradation rate of the filter. These indicators provide more meaningful real time information to the driver or fleet owner than a percentage of the remaining life. The proposed method was validated based on driving data of a production vehicle obtained from Ford big data drive (BDD), combined with simulated degradation profile of the air filter.

Commentary by Dr. Valentin Fuster
2017;():V002T19A004. doi:10.1115/DSCC2017-5367.

Despite the widespread commercialization of Li-ion batteries in various markets including portable electronics, electrified transportation, and stationary energy storage systems, their safety and reliability still poses as a concern in the eyes of industry and general public. There has been great strides in the past few decades in the development of Battery Management Systems (BMSs). The majority of the efforts, however, avoid fault occurrence by conservative designs rather than directly incorporating fault diagnostics in the BMS. Such a functionality in the BMS would enable the detection of the occurrence, type, and location of the faults and therefore, a proper reaction to them. Realizing the need for such a feature in the BMSs, the development of a model-based fault detection scheme is proposed in this paper. This method is formulated based on the original PDEs describing a single particle electrochemical battery model. The use of PDEs in the fault detection scheme reduces uncertainties arising from the model approximation. Furthermore, the convergence of this PDE-based approach is proved using Lyapunov stability theorem. Finally, the effectiveness of the proposed method in detecting various fault types ranging from incipient degradation mechanisms to abrupt faults is illustrated through simulations.

Commentary by Dr. Valentin Fuster
2017;():V002T19A005. doi:10.1115/DSCC2017-5392.

Electro-Hydraulic Systems (EHS) are commonly used in many industrial applications. Prediction and timely fault detection of EHS can significantly reduce their maintenance cost, and eliminate the need for redundant actuators. Current practice to detect faults in the actuators can miss failures with combination of multiple sources. Missed faults can result in sudden, unforeseen failures. We propose a fault detection technique based on Multiple Regressor Adaptive Observers (MRAO). The results were evaluated using a two-stage servo-valve model. The proposed MRAO can be used for on-line fault detection. Therefore, we propose a health monitoring approach based on the trend of the identified parameters of the system. Using the history of identified parameters, normal tear and wear of the actuator can be distinguished from the component failures to more accurately estimate the remaining useful life of the actuator.

Commentary by Dr. Valentin Fuster
2017;():V002T19A006. doi:10.1115/DSCC2017-5408.

As an integral part of electrified powertrain, resolver is broadly used to do position and speed sensing for electric motors, subject to different types of resolver faults. This paper investigates the resolver fault propagation in electrified powertrain, with focus on the amplitude imbalance, quadrature imperfection and reference phase shift in the resolver position sensing system. The resolver fault effects in the Permanent Magnet Synchronous Machine (PMSM) drive system are first analyzed based on the mathematical model of a surface mounted PMSM with direct Field-oriented Control (FOC). Then the resolver fault propagation in the powertrain is studied in terms of two different motor operating conditions, motor torque control and motor speed control. Simulation is done in Matlab/Simulink based on the PMSM drive model and the powertrain-level simulator to verify the fault propagation analyses. The results can be used to help design the resolver fault diagnostic strategy and determine speed matching condition between engine and electric motor for mode transition control in hybrid electric vehicles.

Topics: Engines
Commentary by Dr. Valentin Fuster

Unmanned, Ground and Surface Robotics

2017;():V002T21A001. doi:10.1115/DSCC2017-5086.

This paper presents a steering model for predicting human performance in teleoperating unmanned ground vehicles (UGVs). The task of path following, including lane keeping and curve negotiation, is considered for a UGV teleoperation system. Human steering performance in teleoperation is notably different than steering performance in on-board driving conditions due to considerable communication delays in remote teleoperation systems and limited information teleoperators receive from the vehicle sensory system. This paper adopts a cognitive model that was originally developed for a typical highway driving scenario when driver is on board and develops a tuning strategy to adjust the model parameters without human data to reflect the effect of various latencies and UGV speeds on driver performance in a teleoperated path following task. It is shown that the proposed model with tuning strategy i) can adequately capture the trend of changes in driver performance for different teleoperated driving scenarios ii) is able to predict an expert human teleoperator’s performance across different speeds and latencies considered. Thus, the tuned model can be an appropriate candidate to be used in place of human drivers for the simulation-based evaluation of UGV mobility in teleoperation systems.

Topics: Vehicles
Commentary by Dr. Valentin Fuster
2017;():V002T21A002. doi:10.1115/DSCC2017-5131.

A service issue that can adversely affect the performance of many undersea vehicles in general application is either over or under weight operation. Depth keeping precision can be impacted when a vehicle is launched at a weight different from that specified in the nominal flight control system design. As a result, overall maneuvering performance and the vehicle application objectives can be significantly impacted. This paper presents a compensation method based on simple expansion of the vehicle’s autopilot depth controller trim schedule. Expansion is defined relative to a vehicle’s nominally fixed weight-buoyancy flight control equilibrium trim design point and refers to practical variances in both net buoyancy and buoyancy-weight center geometric offset. This implementation requires only a simple, highly feasible, dry dockside launch under/overweight measurement for operational flight static reference. Off weight compensation is enabled by a priori determination of the vehicle’s steady speed, straight-horizontal flight path, body pitch, and elevator trim angles when subjected to the expected set range of weight-buoyancy variations. The method and implementation are outlined. A depth step change maneuver, using a high fidelity autopilot-software-in-the-loop maneuvering simulation, is examined to verify the implementation feasibility and effectiveness.

Commentary by Dr. Valentin Fuster
2017;():V002T21A003. doi:10.1115/DSCC2017-5159.

Virtual holonomic constraints (VHCs) framework is a recent control paradigm for systematic design of motion controllers for wheel-less biologically inspired snake robots. Despite recent developments for VHC-based control systems for ground and underwater robotic snakes, they employ only two families of propulsive virtual holonomic constraints, i.e., lateral undulatory and eel-like virtual constraints. In this paper we extend the family of propulsive virtual constraints that can be used with VHC-based controllers by presenting a VHC analysis and synthesis methodology for planar snake robots that are subject to ground friction forces. In particular, we present a nonlinear differential inequality that guarantees forward motion of planar snake robots under the influence of VHCs. Furthermore, we provide a family of hyperbolic partial differential equations that can be employed to generate propulsive virtual holonomic constraints for these biologically inspired robots. Simulations are presented to verify the proposed analysis/synthesis methodology.

Topics: Robots , Design
Commentary by Dr. Valentin Fuster
2017;():V002T21A004. doi:10.1115/DSCC2017-5193.

There are many types of systems in both nature and technology that exhibit limit cycles under periodic forcing. Sometimes, especially in swimming robots, such forcing is used to propel a body forward in a plane. Due to the complexity in studying a fluid system it is often useful to investigate the dynamics of an analogous land model. Such analysis can then be useful in gaining insight about and controlling the original fluid system. In this paper we investigate the behavior of the Chaplygin sleigh under the effect of viscous dissipation and sinusoidal forcing. This is shown to behave in a similar manner as certain robotic fish models. We then apply limit cycle analysis techniques to predict the behavior and control the net translational velocity of the sleigh in a horizontal plane.

Topics: Limit cycles
Commentary by Dr. Valentin Fuster
2017;():V002T21A005. doi:10.1115/DSCC2017-5207.

This paper presents the design, modeling, control and navigation for a novel ground-based mobile sensing platform that can collect multi-modal data in agricultural research farms for high throughput modular plant phenotyping. The platform will have the following capabilities (i) Navigate in a row-crop farm to collect data with minimal human intervention during operation (ii) Autonomous decision making i.e, it can take its own decisions for maximizing the value of information of the acquired data and (iii) Scalable in terms of the size of the farmland. The design requirements for such a platform or robot is formulated, and a detailed discussion on realizing such a design is presented. The dynamics of the robot is presented in the state space form and it is abstracted in the form of a control flow diagram for the automatic steering system. An adaptive sampling approach has been taken to generate an estimated belief-space which is leveraged in the proposed opportunistic sensing scheme to generate way-points for navigation.

Commentary by Dr. Valentin Fuster
2017;():V002T21A006. doi:10.1115/DSCC2017-5220.

In recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal agricultural workers. Among all the enabling technologies used in robot operations for agricultural products with shallow structures, controlling a robot to traverse throughout a field is challenging. In this study the motion control of a robot, custom-designed for strawberry fields, is separated into multiple phases to deal with the over-bed and cross-bed operation needs. Different sensors are used for different control phases. In particular, nonlinear robust controllers are designed for the cross-bed motion, purely relying on vision feedback. The proposed sensing and control methods are successfully validated in a commercial farm.

Topics: Robots
Commentary by Dr. Valentin Fuster
2017;():V002T21A007. doi:10.1115/DSCC2017-5283.

This paper presents a motion control algorithm that exploits mutual information and a Bayesian filter to optimally guide a mobile robotic sensor, e.g., an unmanned aerial or ground vehicle (UAV or UGV) with a sensor, to localize an unknown target such as the source of a gas/chemical leak. Specifically, optimal feedforward inputs are found such that with respect to the posterior distribution, the robot moves to minimize uncertainty. The formulation depends on the robot’s dynamics model and the sensor’s stochastic measurement model. Additionally, a utility function is defined such that the estimator’s uncertainty is minimized, i.e., the acquisition of information is maximized. The approach is applied to a single robot with three different sensor models for validation. In particular, for the chemical concentration sensor case a Gaussian plume likelihood model is assumed and simulation results show that a single robot can effectively localize the unknown source, demonstrating the effectiveness of the approach.

Commentary by Dr. Valentin Fuster
2017;():V002T21A008. doi:10.1115/DSCC2017-5324.

This paper presents a monocular vision-based, unsupervised floor detection algorithm for semi-autonomous control of a Hybrid Mechanism Mobile Robot (HMMR). The paper primarily focuses on combining monocular vision cues with inertial sensing and ultrasonic ranging for on-line obstacle identification and path planning in the event of limited wireless connectivity. A novel, unsupervised vision algorithm was developed for floor detection and identifying traversable areas, in order to avoid obstacles in semi-autonomous control architecture. The floor detection algorithms were validated and experimentally tested in an indoor environment under various lighting conditions.

Topics: Mobile robots
Commentary by Dr. Valentin Fuster
2017;():V002T21A009. doi:10.1115/DSCC2017-5330.

This study presents the development and implementation of an autonomous obstacle avoidance algorithm for an UGV (Unmanned Ground Vehicle). This research improves the prior work by enhancing the obstacle avoidance capability to handle moving obstacles as well as stationary obstacles. A mathematical representation of the area of operation with obstacles is formulated by PTEM (Probabilistic Threat Exposure Map). The PTEM quantifies the risk in being at a position in an area with different types of obstacles. A LRF (Laser Range Finder) sensor is mounted on the UGV for obstacle data in the area that is used to construct the PTEM. A guidance algorithm processes the PTEM and generates the speed and heading commands to steer the UGV to assigned waypoints while avoiding obstacles. The main contribution of this research is to improve the PTEM framework by updating it continuously as new LRF readings are obtained, on the contrary to the prior work with fixed PTEM. The improved PTEM construction algorithm is implemented in a MATLAB/Simulink simulation environment that includes models of the UGV, LRF, all the sensors and actuators needed for the control of the UGV. The performance of the algorithm is also demonstrated in real time experiments with an actual UGV system.

Topics: Vehicles
Commentary by Dr. Valentin Fuster
2017;():V002T21A010. doi:10.1115/DSCC2017-5337.

This paper presents modeling and analysis of a quadruped robot that utilizes tail dynamics to control its heading angle. The tail is envisioned to assist locomotion as a means separate from its legs to generate forces and moments to improve performance in terms maneuverability. Tail motion is analyzed for both low and high-speed tail actuation to derive sufficient conditions to maintain equilibrium and formulate maneuverability relations that result in rotation and translation of the robotic system. Sensitivity analysis is presented to select optimal tail mass and length ratios to maximize the change of the heading angle. A heading controller is then proposed and simulated to achieve a desired heading angle utilizing tail dynamics. Results of this research will assist in the design, modeling, and analysis of robotic systems sharing similar topologies to the proposed model, such as mobile robots with wheeled, tracked, multi-legged, or articulated-body based locomotion with swinging extremities such as tails, torsos, and limbs.

Commentary by Dr. Valentin Fuster
2017;():V002T21A011. doi:10.1115/DSCC2017-5349.

Robot system models often have difficulty allowing for direct command over all input degrees of freedom if the system has a large number of imposed constraints. A snake robot with more than three links and a nonholonomic wheel on each link cannot achieve arbitrary configurations in all of its joints simultaneously. For such a system, we assume partial command over a subset of the joints, and allow the rest to evolve according to kinematic chained and dynamic models. Different combinations of commanded and passive joints, as well as the presence of dynamic elements such as torsional springs, can drastically change the coupling interactions and stable oscillations of the joints. We use the oscillation modes that emerge to inform feedback controllers that achieve desired overall locomotion of the robot.

Topics: Robots
Commentary by Dr. Valentin Fuster
2017;():V002T21A012. doi:10.1115/DSCC2017-5352.

Computer vision methods are commonly used to detect and track motion using conventional cameras, however, that is limited with the field of view (FOV) of the camera. This study is to attempt to overcome this challenge by using a 360 degree camera. Our approach utilizes background subtracter from OpenCV Library which creates a continuously updating background model for the motion detection. The model is subtracted from the current frame leaving contours symbolizing the movement observed in the camera view. These contours are then analyzed and processed so that the system can track the largest contour. The tracked movement is outlined and directed to the user via Virtual Reality (VR) headset. The VR headset only displays a 60 degree portion of the camera view to the user which provides more realistic situational awareness of the surroundings for the user. These activities are a part of a larger effort to establish a foundation for autonomous unmanned vehicle systems with situational awareness capabilities.

Commentary by Dr. Valentin Fuster
2017;():V002T21A013. doi:10.1115/DSCC2017-5368.

Localization of mobile robots in GPS-denied envrionments (e.g., underwater) is of great importance to achieving navigation and other missions for these robots. In our prior work a concept of Simultaneous Localization And Communication (SLAC) was proposed, where the line of sight (LOS) requirement in LED-based communication is exploited to extract the relative bearing of the two communicating parties for localization purposes. The concept further involves the use of Kalman filtering for prediction of the mobile robot’s position, to reduce the overhead in establishing LOS. In this work the design of such a SLAC system is presented and experimentally evaluated in a two-dimensional setting, where a mobile robot localizes itself through wireless LED links with two stationary base nodes. Experimental results are presented to demonstrate the feasibility of the proposed approach and the important role the Kalman filter plays in reducing the localization error. The effect of the distance between the base nodes on the localization performance is further studied, which bears implications in future SLAC systems where mobile base nodes can be reconfigured adaptively to maximize the localization performance.

Commentary by Dr. Valentin Fuster
2017;():V002T21A014. doi:10.1115/DSCC2017-5372.

This paper focuses on trajectory tracking control for an under-actuated car-like ground vehicle. We consider the 3 degree-of-freedom nonlinear vehicle rigid-body dynamics with nonlinear tire traction force, nonlinear drag forces and actuator dynamics. The control structure employs multi-loop Trajectory Linearization Control (TLC) based on singular perturbation (time-scale separation) theory for exponential stability. The designed controller controls the longitudinal velocity and steering angle simultaneously to follow a feasible guidance trajectory. The paper presents the modeling and design approach with computer simulation results on a scaled-down model car.

Commentary by Dr. Valentin Fuster
2017;():V002T21A015. doi:10.1115/DSCC2017-5376.

Trajectory tracking guidance and control for nonholonomic (car-like) Autonomous Ground Vehicles (AGV), such as self-driving cars and car-like wheeled mobile robots, is a more challenging control problem than path following control, because the latter does not impose a speed requirement on the vehicle motion. The tracking error dynamics along the nominal path are nonlinear and time-varying in nature, which need to be exponentially stabilized. This paper presents a Line-of-Sight (LOS) Pure-Pursuit Guidance (PPG) trajectory design algorithm that generates a three Degrees of Freedom (DOF) spatial trajectory for an AGV equipped with a 3DOF trajectory tracking controller. The LOS PPG can be used for cooperative, passive (neutral) and adversarial tracking tasks, such as, respectively, formation driving, autonomous lane keeping with speed requirement, and chasing an evading vehicle. The algorithm is verified with computer simulations on a 1/6 scale electric car model, and will be further validated on that model car in the near future.

Commentary by Dr. Valentin Fuster

Motion and Vibration Control Applications

2017;():V002T23A001. doi:10.1115/DSCC2017-5096.

In hard disk drives (HDDs), there exist multiple mechanical resonances whose central frequencies may shift due to the change of environmental conditions such as the temperature. Such slowly varying resonance frequencies, if not handled properly, may degrade the positioning accuracy and even result in the instability of the closed-loop HDD system. Therefore, it is important to identify these resonance frequencies efficiently without interrupting the reading/writing process in HDDs. One main challenge of the frequency identification in a dual-stage HDD lies in the fact that it is a double-input-single-output (DISO) system. The outputs of the voice coil motor (VCM) and the piezoelectric microactuator (PZT) are coupled together. This paper proposes a practical strategy to identify the resonance frequencies in both the VCM and the PZT without disabling the PZT control process. Bandpass filters are utilized to separate the overall position error signal (PES) into several frequency segments based on priorly-known frequency range for each resonance. Two standard parameter adaptation algorithms are studied and discussed. Simulation results validate the effectiveness of proposed identification strategy.

Topics: Resonance , Disks
Commentary by Dr. Valentin Fuster
2017;():V002T23A002. doi:10.1115/DSCC2017-5103.

In this paper, we propose a finite-impulse-response (FIR)-based feedforward control approach to mitigate the acoustic-caused probe vibration during atomic force microscope (AFM) imaging. Compensation for the extraneous probe vibration is needed to avoid the adverse effects of environmental disturbances such as acoustic noise on AFM imaging, nanomechanical characterization, and nanomanipulation. Particularly, residual noise still exists even though conventional passive noise cancellation apparatus has been employed. The proposed technique exploits a data-driven approach to capture both the noise propagation dynamics and the noise cancellation dynamics in the controller design, and is illustrated through the experimental implementation in AFM imaging application.

Commentary by Dr. Valentin Fuster
2017;():V002T23A003. doi:10.1115/DSCC2017-5203.

It is difficult for crane operators to lift and maneuver payloads without causing significant, uncontrolled motion. Consequently, research in the area of crane operation has focused on designing controllers to minimize payload swing. However, lifting long and slender payloads (e.g., steel I-beams) from a non-level surface (e.g., like many outdoor construction sites) has not been addressed in much detail. This paper evaluates the amplitude of residual swing and robustness of two different control methodologies while hoisting a slender payload up into the air from an inclined surface. A semi-automatic approach, where the crane operator controls the lift direction and a proportional-integral-derivative (PID) controller adjusts the overhead trolley position, was developed. Experimental tests demonstrate that this method reduces the peak amplitude of residual vibration by about 80% for most non-zero incline angles.

Topics: Cranes
Commentary by Dr. Valentin Fuster
2017;():V002T23A004. doi:10.1115/DSCC2017-5247.

The control of boom cranes is a topic that has generated a significant amount of research. Particularly, cranes mounted on ocean-going ships pose a significant challenge. Due to the harmonic disturbance resulting from ocean conditions, open-loop control methods such as input shaping have been largely ignored in this area of research. This work will develop linearized governing equations for a planar, harmonically-excited boom crane. Using these approximations, a command-shaping strategy that minimizes payload deflection during and shortly after a luff command will be presented. It is anticipated that this method will be used to smooth the transition to a closed-loop controller which engages after the operator-given command is complete.

Commentary by Dr. Valentin Fuster
2017;():V002T23A005. doi:10.1115/DSCC2017-5377.

Wind-induced vibration of power lines has been a major challenge for design engineers for decades. Hitherto, there is no effective devices that can suppress these vibrations throughout a wide range of resonant frequencies. This paper presents a promising vibration suppression technique using an energy harvester moving vibration absorber (EHMVA), which can simultaneously harvest energy and suppress the vibrations. The vibration-based energy harvesting can be achieved using an electromagnetic transducer, which replaces the viscous damping element of conventional absorbers. This harvested energy can then be utilized to power small sensors and electronic devices required for EHMVA to adapt to wind characteristics and move to an optimum location, thus leading to potentially superior vibration control. The coupled dynamics between a single conductor and EHMVA is presented and numerical examples are carried out to investigate the performance of the proposed absorber. The findings are very promising and open a horizon of future opportunities to optimize the design of EHMVAs for superior performance.

Commentary by Dr. Valentin Fuster

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