Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor

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 parameter estimation. We develop a parameter-adaptive reference governor (PARG) that modifies the driver steering input to enforce a rollover avoidance constraint, and state and input constraints. We design a recursive Bayesian estimator that produces confidence estimates of the parameters, including the center-of-gravity height. The confidence estimates inform a supervised learning algorithm, which constructs online constraint admissible sets that are lever- aged by the PARG to ensure rollover prevention. Simulation results on a Fishhook maneuver show that the method robustly prevents rollover, and that the resulting parameter estimates are contained in the confidence sets produced by the Bayesian estimator.