Observer design with performance guarantees for vehicle control purposes via the integration of learning-based and LPV approaches

Abstract

The paper proposes an enhanced observer design method for autonomous vehicles, with which the unmeasurable states in vehicle and chassis motion can be estimated. The novelty of the method is that the learning-based observer and the linear parameter varying (LPV) observer in a joint observer design structure are incorporated, which results in an improved performance level on the estimation error. Nevertheless, the proposed design method is able to guarantee the limitation of the estimation error, even if the error of the learning-based observer under all scenarios cannot be verified. Thus, the proposed method handles the main disadvantage of the learning-based approaches, i.e. the lack of performance guarantees, while their advantages, i.e. the improved observation performance in the operation of the observer are taken. The proposed method is applied on a lateral path following control problem, where the goal of the observer is to provide an accurate lateral velocity signal for the vehicle. The effectiveness of the method is illustrated through simulation examples on high- fidelity vehicle dynamic simulator CarSim.