Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems

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. A critic neural network is used to approximate the cost and an actor neural network is used to approximate the optimal event-triggered controller. Since in the algorithm proposed there are dynamics that exhibit continuous evolutions described by ordinary differential equations and instantaneous jumps or impulses, we will use an impulsive system approach. A Lyapunov stability proof ensures that the closed-loop system is asymptotically stable. Finally, we illustrate the effectiveness of the proposed solution compared to a time-triggered controller.