Self-learning control of model uncertain active suspension systems with observer–critic structure

Abstract

This paper presents a self-learning control algorithm for model uncertain suspension systems using single network adaptive critic (SNAC) approach. First, a differential neural network (DNN) observer in conjunction with the weight updating law is established to observe the uncertain dynamic. Then, the nominal optimal value function is approximated by a critic NN whose weight is updated by a novel design learning law driven by the filtered parameter error. The online self-learning control policy is thus derived by approximately solving the Hamilton–Jacobi–Bellman (HJB) equation based on SNAC technique. The Lyapunov approach is synthesized to ensure the convergent characteristics of the entire closed-loop system composed of the DNN observer and the self-learning control policy. Computer simulation of a quarter car suspension system is established to verify the effectiveness of the proposed approach. Simulation results illustrated that the designed method can ensure the good performance in terms with the road hold and ride quality. In addition, independent of model and online self-learning characteristics make it possible to design a high-performance vehicle active suspension controller.