Predictor-based model reference adaptive control for a vehicle lateral dynamics considering uncertainties

This article deals with the design and analysis of state predictor-based model reference adaptive control for a vehicle lateral dynamics. The goal of this article is to achieve the desired tracking in the presence of uncertainty in order to guarantee the stability as well as improving the performance of the vehicle. Through Lyapunov stability analysis, the adaptive laws of stable predictor-based state feedback have been derived. In order to evaluate the advantages of proposed controller, first the performance of non-adaptive linear quadratic regulator controller in nominal conditions has been presented. By adding uncertainty to the vehicle dynamics, the performance of controller has been reduced. Then, by augmenting model reference adaptive control to linear quadratic regulator, the performance has been improved. Finally, a considerable improvement has been achieved in transient response and control signal using predictor-based model reference adaptive control. Simulation results show that by adding predictor-based model reference adaptive control to the system, uncertain part of the vehicle dynamics is approximated, and the tracking structure of integrated control (linear quadratic regulator + predictor-based model reference adaptive control) has been successful.