TR2021-151

Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor


    •  Berntorp, K., Chakrabarty, A., Di Cairano, S., "Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor", IEEE Annual Conference on Decision and Control (CDC), December 2021.
      BibTeX TR2021-151 PDF
      • @inproceedings{Berntorp2021dec,
      • author = {Berntorp, Karl and Chakrabarty, Ankush and Di Cairano, Stefano},
      • title = {Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-151}
      • }
  • MERL Contacts:
  • Research Areas:

    Control, Dynamical Systems, Machine Learning

Abstract:

This paper describes an approach to the vehicle rollover prevention problem that includes estimation of parameters affecting the roll dynamics and a controller accounting for uncertainties in such parameters. We develop an adaptive reference governor (ARG) that modifies the driver steering input based on satisfaction of a rollover avoidance constraint, and state and input constraints. The vehicle dynamics are highly nonlinear and has parametric uncertainties, for which the presented approach ensures rollover prevention. We design a recursive Bayesian estimator that produces confidence estimates of the parameters, including the center-of-gravity height. The confidence estimates are used to construct online constraint admissible sets, which are leveraged by the ARG to ensure rollover prevention. Simulation results on a Fishhook maneuver show that the method robustly avoids rollover prevention, and that the resulting parameter estimates are contained in the confidence sets.