Event‐triggered‐based integral reinforcement learning output feedback optimal control for partially unknown constrained‐input nonlinear systems

Abstract In this paper, an adaptive output feedback event‐triggered optimal control algorithm is proposed for partially unknown constrained‐input continuous‐time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event‐triggered condition is established, and only when the event‐triggered condition is violated will the event be triggered and the state be sampled. Then, an event‐triggered‐based synchronous integral reinforcement learning (ET‐SIRL) control algorithm with critic‐actor neural networks (NNs) architecture is proposed to solve the event‐triggered Hamilton–Jacobi–Bellman equation under the established event‐triggered condition. The critic and actor NNs are used to approximate cost function and optimal event‐triggered optimal control law, respectively. Meanwhile, the event‐triggered‐based closed‐loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET‐SIRL control algorithm.