Optimal control of partially unknown constrained‐input systems: A dynamic event‐triggered‐based approach

Abstract

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.