August 16, 2025

Adaptive Neural Network Optimal Event‐Triggered Control for Unmanned Surface Vehicle Based on Zero‐Sum Game

JZJiaming ZhangHebei Medical University WZWenjun ZhangSouth China Agricultural University STShaocheng TongLiaoning University of Technology

Key Points

Adaptive neural network control ensures optimal performance despite uncertainties in the unmanned surface vehicle system.
Simulation results show that the event-triggered control can significantly reduce actuator updates while maintaining performance.
The proposed method integrates dynamic programming and zero-sum game theory for effective feedback control design.
Event-triggered mechanisms validate that the controlled unmanned surface vehicle system is uniformly ultimately bounded.

Abstract

ABSTRACT This article develops an adaptive neural network (NN) optimal event‐triggered control strategy for an unmanned surface vehicle (USV) system with uncertain dynamics and unknown external disturbances. A new event‐triggered NN optimal control policy is developed, which is composed of the NN adaptive feed‐forward controller and an optimal error feedback control policy. The former is constructed by adopting the backstepping control design technique, and the latter is designed by using adaptive dynamic programming theory and a zero‐sum game. Event‐triggered mechanisms are designed to reduce the updating times of actuators. It is demonstrated that the developed optimal control strategy can ensure the controlled USV system is uniformly ultimately bounded (UUB) and achieve optimal control performance. Simulation and comparison results are given to verify the validity of the developed optimal control scheme.

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