Self-learning robust optimal control for continuous-time nonlinear systems with mismatched disturbances

This paper presents a novel adaptive dynamic programming(ADP)-based self-learning robust optimal control scheme for input-affine continuous-time nonlinear systems with mismatched disturbances. First, the stabilizing feedback controller for original nonlinear systems is designed by modifying the optimal control law of the auxiliary system. It is also demonstrated that this feedback controller can optimize a specified value function. Then, within the framework of ADP, a single critic network is constructed to solve the Hamilton-Jacobi-Bellman equation associated with the auxiliary system optimal control law. To update the critic network weights, an indicator function and a concurrent learning technique are employed. By using the proposed update law for the critic network, the restrictive conditions including the initial admissible control and the persistence of excitation condition are relaxed. Moreover, the stability of the closed-loop auxiliary system is guaranteed in the sense that all the signals are uniformly ultimately bounded. Finally, the applicability of the developed control strategy is illustrated through simulations for an unstable nonlinear plant and a power system.