TR2020-005
Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control
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- "Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control", Journal of Vehicle Systems Dynamics, DOI: 10.1080/00423114.2019.1697456, January 2020.BibTeX TR2020-005 PDF
- @article{Berntorp2020jan,
- author = {Berntorp, Karl and Quirynen, Rien and Uno, Tomoki and Di Cairano, Stefano},
- title = {Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control},
- journal = {Journal of Vehicle Systems Dynamics},
- year = 2020,
- month = jan,
- doi = {10.1080/00423114.2019.1697456},
- url = {https://www.merl.com/publications/TR2020-005}
- }
,
- "Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control", Journal of Vehicle Systems Dynamics, DOI: 10.1080/00423114.2019.1697456, January 2020.
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MERL Contacts:
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Research Areas:
Control, Dynamical Systems, Machine Learning, Signal Processing
Abstract:
We propose an adaptive nonlinear model predictive control (NMPC) for vehicle tracking control. The controller learns in real time a tire force model to adapt to a varying road surface that is only indirectly observed from the effects of the tire forces determining the vehicle dynamics. Learning the entire tire model from data would require driving in the unstable region of the vehicle dynamics with a prediction model that has not yet converged. Instead, our approach combines NMPC with a noise-adaptive particle filter for vehicle state and tire stiffness estimation and a pre-determined library of tire models. The stiffness estimator determines the linear component of the tire model during normal vehicle driving, and the control strategy exploits a relation between the tire stiffness and the nonlinear part of the tire force to select the appropriate full tire model from the library, which is then used in the NMPC prediction model. We validate the approach in simulation using real vehicle parameters, demonstrate the real-time feasibility in automotive-grade processors using a rapid prototyping unit, and report preliminary results of experimental validation on a snow-covered test track.
Related News & Events
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NEWS Rien Quirynen gives invited talk at ELO-X Workshop on Embedded Optimization and Learning for Robotics and Mechatronics Date: October 10, 2022 - October 11, 2022
Where: University of Freiburg, Germany
Research Areas: Control, Machine Learning, OptimizationBrief- Rien Quirynen is an invited speaker at an international workshop on Embedded Optimization and Learning for Robotics and Mechatronics, which is organized by the ELO-X project at the University of Freiburg in Germany. This talk, entitled "Embedded learning, optimization and predictive control for autonomous vehicles", presents recent results from multiple projects at MERL that leverage embedded optimization, machine learning and optimal control for autonomous vehicles.
This workshop is part of the ELO-X Fall School and Workshop. Invited external lecturers will present state-of-the-art techniques and applications in the field of Embedded Optimization and Learning. ELO-X is a Marie Curie Innovative Training Network (ITN) funded by the European Commission Horizon 2020 program.
- Rien Quirynen is an invited speaker at an international workshop on Embedded Optimization and Learning for Robotics and Mechatronics, which is organized by the ELO-X project at the University of Freiburg in Germany. This talk, entitled "Embedded learning, optimization and predictive control for autonomous vehicles", presents recent results from multiple projects at MERL that leverage embedded optimization, machine learning and optimal control for autonomous vehicles.