Neural dynamic programming for event-based nonlinear adaptive robust stabilization

Abstract

In this paper, we develop an event-based adaptive robust stabilization method for continuous-time nonlinear systems with uncertain terms via a self-learning technique called neural dynamic programming. Through system transformation, it is proven that the robustness of the uncertain system can be achieved by designing an event-triggered optimal controller with respect to the nominal system under a suitable triggering condition. Then, the idea of neural dynamic programming is adopted to perform the main controller design task by building and training a critic network. Finally, the effectiveness of the present adaptive robust control strategy is illustrated via a simulation example.