An online actor/critic algorithm for event-triggered optimal control of continuous-time nonlinear systems

Abstract

This paper proposes a novel optimal adaptive event-triggered control algorithm for nonlinear continuous-time systems. The goal is to reduce the controller updates, by sampling the state only when an event is triggered to maintain stability and optimality. The online algorithm is implemented based on an actor/critic neural network structure. The algorithm proposed exhibits dynamics with continuous evolutions described by ordinary differential equations and instantaneous jumps. A Lyapunov stability proof ensures that the closed-loop system is asymptotically stable.