Optimal Event-Triggered Control of Constrained Nonlinear Systems

We conduct a discussion on a kind of constrained optimal event-triggered control (ETC) problems. Initially, by taking the variable substitution, we change the input signal's asymmetric constraints into its symmetric constraints. Then, we introduce an event-triggering mechanism, and then present an event-triggered Hamilton-Jacobi-Bellman equation (ET-HJBE). To solve the ET-HJBE, we only use a critic neural network (CNN) with its weight tuned under the reinforcement learning framework. After that, we prove that the closed-loop system and the CNN's weight error are uniformly ultimately bounded. Finally, we validate the present ETC strategy via numerical simulations.