Disturbance Compensation-Based Optimal Tracking Control for Perturbed Nonlinear Systems

This paper investigates the disturbance compensation-based optimal tracking control for nonlinear systems in the presence of uncertain dynamic drift and extraneous disturbance by using the adaptive dynamic programming (ADP). First, an extended state observer (ESO)-based disturbance rejection controller is designed to estimate the comprehensive disturbances of system. Then, a novel composite controller capable of online learning is developed based on disturbance rejection controller and optimal regulation law, where the optimal regulation law is conducted by ADP framework to stabilize the dynamics of tracking error and minimize predefined value function. Particularly, an improved critic-only weight updating algorithm is inserted in ADP for ensuring the finite time convergence of critic weight without resorting to traditional actor-critic structures enduring remarkable computational burden. Based on Lyapunov analysis, it is proved that the tracking errors and weight estimation errors of critic network are uniformly ultimately bounded and the pursued controller approximates to the optimal policy. Finally, simulation results are shown to check the superiority of involved strategy, and the value function can be decreased by 25% with consistent tracking performance.