Robust optimal control for a class of nonlinear systems with unknown disturbances based on disturbance observer and policy iteration - Song, Ruizhuo; Lewis, Frank L. (Neurocomputing (Amsterdam), 2020)
A robust optimal control method for a class of nonlinear systems with unknown disturbances is addressed in this paper. In this framework, adaptive dynamic programming (ADP) is presented to obtain the optimal control. On-policy learning allows the performance index function and the optimal control to be obtained iteratively. It is shown that the iterative performance index function is non-increasing. A nonlinear disturbance observer is designed to estimate external disturbances. The compensation control is used to compensate for the influence of the disturbances. It is proven that the disturbance observer error is exponentially stable, under some conditions. The properties of the nonlinear system with unknown disturbance steered by the robust optimal control input are also proven. Simulation results demonstrate the performance of the proposed robust optimal control scheme for the nonlinear system with unknown disturbance.