Event-based robust performance guarantee for nonaffine plants via system identification

Abstract

In this paper, by making use of recurrent neural networks (RNNs), a novel event-based robust optimal controller is designed for a category of continuous-time (CT) nonlinear nonaffine systems with disturbances. For tackling the optimal-robust problem transformation and the performance guarantee, a system identifier is developed to reconstruct the system, and an event-triggered critic-only strategy is investigated to replace the traditional time-based actor-critic structure. The RNN is adopted to reconstruct the nonaffine system as an identifier for obtaining the affine model. It simplifies the system structure and reduces computational burdens. In addition, the stability of the unknown nominal system learned by the RNN is proved. Then, using the obtained system identifier and the adaptive critic learning structure, the proposed event-triggered robust control with performance guarantee is realized. At last, the effectiveness of the given design method is verified by two simulation examples.