TR2023-064
Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles
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- "Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles", Vehicle System Dynamics, DOI: 10.1080/00423114.2023.2219791, May 2023.BibTeX TR2023-064 PDF
- @article{Vaskov2023may2,
- author = {Vaskov, Sean and Quirynen, Rien and Menner, Marcel and Berntorp, Karl},
- title = {Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles},
- journal = {Vehicle System Dynamics},
- year = 2023,
- month = may,
- doi = {10.1080/00423114.2023.2219791},
- url = {https://www.merl.com/publications/TR2023-064}
- }
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- "Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles", Vehicle System Dynamics, DOI: 10.1080/00423114.2023.2219791, May 2023.
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Research Areas:
Abstract:
This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We propose a stochastic nonlinear model predic- tive controller (SNMPC) that learns a tire–road friction model online using standard automotive-grade sensors. Learning the entire tire–road friction model in real time requires driving in the nonlinear, potentially unstable regime of the vehicle dynam- ics, using a prediction model that may not have fully converged. To handle this, we formulate the tire-friction model learning in a Bayesian framework, and propose two estimators that learn different aspects of the tire–road friction. The estimators out- put the estimate of the tire-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes. The SN- MPC exploits the uncertainty estimate in its prediction model to take proper action when the uncertainty is large. We validate the approach in an extensive Monte-Carlo study using real vehicle parameters and in CarSim. The results when comparing to various MPC approaches indicate a substantial reduction in constraint violations, as well as a reduction in closed-loop cost. We also demonstrate the real-time feasibility in automotive-grade processors using a dSPACE MicroAutoBox-II rapid prototyping unit, showing a worst-case computation time of roughly 40ms.