Event-triggered integral reinforcement learning for nonlinear continuous-time systems

In this paper, the optimal control problem for the continuous-time nonlinear systems with partially unknown dynamics is investigated. The event-triggered internal reinforcement learning (IRL) is proposed to approach the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Note that the knowledge of internal dynamics is relaxed, and the event-triggered control scheme is adopted to reduce the computational burden and communication resources. For the online implementation purpose, a single-critic neural network (NN) structure is constructed to approach the optimal value function and the optimal policy with convergence analysis. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed algorithm.