Reinforcement Learning-Based Optimized Tracking Control for Stochastic Nonlinear Strict-Feedback Systems With Wiener and Poisson Noises

This article investigates the optimal control problem of a stochastic nonlinear strict-feedback system subjected to both Wiener and Poisson noises. Since the strict-feedback system does not satisfy the matching condition and involves unknown nonlinear terms and unmeasurable stochastic noises in modeling, an optimized backstepping (OB) technique based on the reinforcement learning (RL) strategy is adopted to design the controller within the identifier–critic–actor architecture. However, the OB technique needs to construct all the virtual and actual controllers as the optimal solutions of their respective subsystems, which inevitably increases the complexity of the algorithm. To alleviate this situation, a novel RL method is proposed, so that the optimized control is unaffected by the disturbances induced by both Wiener and Poisson noises. In addition, an adaptive neural network identifier is incorporated into the RL framework to ensure that the proposed control scheme can be smoothly applied to the unknown nonlinear dynamic system. Finally, a vehicle tracking control example is presented to demonstrate the effectiveness of the proposed method.