Optimal Event-Triggered $H_{\infty}$ Control for Nonlinear Systems with Completely Unknown Dynamics

In this paper, a novel identifier-critic-based event-triggered control method is proposed to solve the
$H_{\infty}$
control problem of unknown nonlinear systems. First, system identification is proposed to reconstruct the completely unknown system dynamics, and neural networks (NNs) based critic learning framework is established to online approximated cost function and obtain corresponding optimal control law. It is highlighted that new NNs weights adaption rules based on a parameter estimation method are designed for the identifier-critic framework. In the meantime, a static event-triggered mechanism is integrated into the proposed identifier-critic architecture to improve communication and computation efficiency. The stability of the control system and the convergence of NN weights are both theoretically proved under the proposed control method. Simulation studies are provided to demonstrate the effectiveness of the proposed
$H_{\infty}$
control method.