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This article presents an identifier‐based dynamic event‐triggered optimal control scheme for partially unknown constrained‐input systems. First, an event‐triggered‐based neural network (NN) identifier is constructed to estimate the unknown system dynamics. Then, an adaptive dynamic programming algorithm with actor‐critic NN structure is adopted to obtain an approximate solution of the Hamilton–Jacobi–Bellman equation. The above considers that transmitted measurements are only available at the triggering instants, and the update of all three NN weights depends on the established dynamic event‐triggered mechanism. Different from existing static event‐triggered mechanism, the proposed dynamic event‐triggered mechanism can further obtain a reasonable trade‐off between performance and communication resources by introducing a dynamic variable, and the Zeno behavior can be excluded by devising an exponential term. It is proved that all the closed‐loop system signals are uniformly ultimately bounded under the established event‐triggered mechanism. Finally, two numerical examples are provided, including the spring‐mass‐damper system, to validate the proposed control scheme.

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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.