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Journal of System Simulation

Abstract

Abstract: An intelligent policy iteration tracking control method is proposed for the tracking control problem with bounded time-varying disturbances. An adaptive disturbance compensator is designed to counteract the bounded disturbance and guarantee the validity of the Hamilton-Jacobi-Bellman (HJB) equation. An identifier network is proposed to estimate the unknown vehicle dynamics, and a new HJB equation is derived using the reconstructed identifier tracking error. An online optimal tracking control strategy for unmanned vehicles is obtained in the state of identifier estimation with the assistance of actor-critic network. Based on Lyapunov theory, it is demonstrated that the identifier tracking error, identifier approximation error and neural network weight errors are all semi-globally uniformly ultimately bounded, and the unmanned vehicle can achieve ideal tracking performance. Simulation results indicate that with a disturbance upper-bounded by 10.331 1 N, the tracking error converges to at least 0.054 8 m which exhibits better anti-interference performance. Compared with sliding mode control, tracking accuracy is improved by 40% and control cost is reduced by 22%.

First Page

1063

Last Page

1075

CLC

TP391.9

Recommended Citation

Huang Jie, Huang Jie. An Intelligent Tracking Control Method for Unmanned Vehicles with Timevarying Disturbances[J]. Journal of System Simulation, 2025, 37(4): 1063-1075.

DOI

10.16182/j.issn1004731x.joss.23-1494

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