Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor

    •  Berntorp, K., Chakrabarty, A., Di Cairano, S., "Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/​CDC45484.2021.9683770, December 2021, pp. 635-640.
      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 Conference on Decision and Control (CDC)},
      • year = 2021,
      • pages = {635--640},
      • month = dec,
      • doi = {10.1109/CDC45484.2021.9683770},
      • url = {}
      • }
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  • Research Areas:

    Control, Dynamical Systems, Machine Learning


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.