Experience Replay Based Online Adaptive Robust Tracking Control for Partially Unknown Nonlinear Systems with Asymmetric Constrained-Input

Abstract

This paper solves the robust tracking problem (RTP) for a type of partially unknown nonlinear systems with asymmetric constrained-input by utilizing an improved adaptive dynamic programming (ADP) method based on experience replay (ER) technique and critic-only neural network (NN). Initially, an identifier neural network (INN) is set to identify the unknown parts of the system dynamics. Subsequently, the tracking error and the desired trajectory are used to construct an augmented system, so that the robust tracking problem (RTP) is transformed into a constrained optimal control problem (OCP). It is proved that the designed control policy of OCP can make the tracking error to be uniformly ultimately bounded (UUB). Then, using the framework of ADP and critic-only NN to solve the derived Hamilton-Jacobi-Bellman equation (HJBE). The NN weight regulation law is partially derived by using gradient descent algorithm (GDA) and then is improved by using the ER technique and the Lyapunov stability theory, which no longer need the conditions of persistence of excitation (PE) and initial admissible control. Besides, the total system states and NN weights is proved to be closed stable by utilizing the Lyapunov technique. Finally, through two simulation examples, it is demonstrated that the proposed control scheme is effective.