Event-driven optimal control for uncertain nonlinear systems with external disturbance via adaptive dynamic programming

This paper investigates the optimal control of uncertain nonlinear systems with external disturbance via adaptive dynamic programming, where two kinds of controllers are introduced and event-driven strategy is used to update the controllers as well as the network weights. One controller is constrained-input, and another is used to offset the external disturbance. Three kinds of neural networks (identifier network, critic network and actor network) are used to approximate the uncertain dynamics, the optimal value, control inputs and external disturbance, respectively. To the best knowledge of the authors, the advantage of this paper is that almost all the existing systems about optimal control via adaptive dynamic programming can be considered as special cases of this system, where the external disturbance is considered. By designing appropriate parameters, the uncertain nonlinear system is asymptotically stable with the event-driven controller. And, under the given learning rates of neural networks, the system state and the estimation error of neural networks are proved to be uniform ultimate boundedness (UUB) via adaptive dynamic programming. Finally, numerical simulation results are presented to verify the theoretical analysis.