Optimal Control of Nonlinear Multi-input Systems with Event-Triggered

A prescribed performance reinforcement learning control is presented based on event-triggered mechanism for the nonlinear multi-input systems, where target error is constrained to a bounded set. Firstly, by using prescribed performance function, the constrained optimal control problem is reformulated as an unconstrained stationary optimal problem. Then, the event-triggered mechanism is integrated to save communication resources, reducing data transmission volume. In order to study the solution of the Hamilton-Jacobi-Bellman equation (HJB), we use a reinforcement learning algorithm based on the single-critic neural network (NN) and introduce a new adaptive law to update the NN weight. Finally, the correctness and effectiveness of the method are proved by a numerical simulation example.