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Road-Vehicles Control Logic Integrating Real Time Multibody Model Follower

[+] Author Affiliations
Isabel Ramirez Ruiz, Edoardo Sabbioni, Francesco Braghin, Federico Cheli

Politecnico di Milano, Milano, Italy

Paper No. DETC2015-47322, pp. V003T01A013; 9 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 17th International Conference on Advanced Vehicle Technologies; 12th International Conference on Design Education; 8th Frontiers in Biomedical Devices
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5710-6
  • Copyright © 2015 by ASME


The challenge to enhance the vehicle driving and handling with a state estimation and prediction system is presented by fusing a primary real time multibody vehicle model capable of providing a good indication of vehicle stability and control, and a secondary model able to estimate the vehicle state from vehicle real and virtual sensors to correct the indications of the primary model. A mathematical algorithm combines these two models in the drive control system improving the behavior of the active systems of the vehicle.

A Multibody vehicle model has been used to achieve a high fidelity simulation of vehicle dynamics. The selected software is LMS.Virtual.Lab Motion with Real-Time Solver which complements the AMESim Real-Time Solver to handle complex real-time 3D-1D mechatronic systems without any simplified conceptual models.

A Sensor Signal Processing Model has been developed to estimate the vehicle states and calculating tire-road contact forces and vehicle sideslip angle. The methodological approach uses the equations of motion of the chassis applying the fundamental principles of classical physics: Newtonian method and Euler angles.

The control logic is based on the continuous updating of the preview multibody vehicle model by the controller sensors information network, which makes the model forecast behavior closer to the real one and improve comfort and linearity of the vehicle response. The driver inputs (throttle, steer angle and torque, brake, gear) are the same for the MBS real time model and for the real vehicle. A first training logic updates the MBS model based on the real vehicle behavior calculated by the sensor network, where the logic has to update in the MBS model just the factors depending on the vehicle itself (for example car weight, tire temperature, shock absorber damping forces, tires characteristics) and to understand and keep into account different environment variation (wet / dry surface). If the real vehicle is equipped with active control systems to improve handling and stability, as active camber control, drive by wire, ESP, Body movement active controls, the real time multibody model will interact with the models 1D or 3D of these vehicle dynamics controls and will improve their performance with a very high accuracy prediction of their influence on vehicle dynamic response.

In conclusion with the help of the preview multibody vehicle model the drive control logic will increase the performance and drive ability of the vehicle with smart logic interacting with all the active systems.

Copyright © 2015 by ASME



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