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This paper proposes a robust adaptive control strategy to address the finite-time containment control problem for underactuated multi-unmanned surface vehicles (USVs) subject to lumped uncertainties and actuator saturation. The strategy employs sliding mode control (SMC) as its core framework, establishing sliding mode surfaces and reaching laws that can achieve finite-time convergence of containment errors. To address the lumped uncertainties, Radial Basis Function (RBF) neural networks are designed for online adaptive compensation. Furthermore, to specifically handle the issue of actuator saturation, an auxiliary dynamic system is introduced to actively compensate for its adverse effects.The simulation results demonstrate the feasibility and effectiveness of the proposed strategy. This research provides an effective solution for USV formations to perform collaborative encirclement tasks under complex constrained environments, such as in scenarios of dynamic confrontation or area denial.

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A Containment Control Strategy for Multi-USVs Based on RBF Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Ji Zhang,
  • Rui Song,
  • Dong Qu,
  • Dongke Liu,
  • Yuxuan Zhong

摘要

This paper proposes a robust adaptive control strategy to address the finite-time containment control problem for underactuated multi-unmanned surface vehicles (USVs) subject to lumped uncertainties and actuator saturation. The strategy employs sliding mode control (SMC) as its core framework, establishing sliding mode surfaces and reaching laws that can achieve finite-time convergence of containment errors. To address the lumped uncertainties, Radial Basis Function (RBF) neural networks are designed for online adaptive compensation. Furthermore, to specifically handle the issue of actuator saturation, an auxiliary dynamic system is introduced to actively compensate for its adverse effects.The simulation results demonstrate the feasibility and effectiveness of the proposed strategy. This research provides an effective solution for USV formations to perform collaborative encirclement tasks under complex constrained environments, such as in scenarios of dynamic confrontation or area denial.