Reinforcement learning-based event-triggered optimal control for unknown nonlinear systems with input delay

The optimal control issue of discrete-time nonlinear unknown systems with time-delay control input is the focus of this work. In order to reduce communication costs, a reinforcement learning-based event-triggered controller is proposed. By applying the proposed control method, closed-loop system's asymptotic stability is demonstrated, and a maximum upper bound for the infinite-horizon performance index can be calculated beforehand. The event-triggered condition requires the next time state information. In an effort to forecast the next state and achieve optimal control, three neural networks (NNs) are introduced and used to approximate system state, value function, and optimal control. Additionally, a M NN is utilized to cope with the time-delay term of control input. Moreover, taking the estimation errors of NNs into account, the uniformly ultimately boundedness of state and NNs weight estimation errors can be guaranteed. Ultimately, the validity of proposed approach is illustrated by simulations.