TR2023-064

Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles


    •  Vaskov, S., Quirynen, R., Menner, M., Berntorp, K., "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}
      • }
  • Research Areas:

    Control, Dynamical Systems, Optimization

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.

 

  • Related Publication

  •  Vaskov, S., Quirynen, R., Menner, M., Berntorp, K., "Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles", arXiv, May 2023.
    BibTeX arXiv
    • @article{Vaskov2023may,
    • author = {Vaskov, Sean and Quirynen, Rien and Menner, Marcel and Berntorp, Karl},
    • title = {Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles},
    • journal = {arXiv},
    • year = 2023,
    • month = may,
    • url = {https://arxiv.org/abs/2305.03798}
    • }