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<p>This paper focuses on challenges in underactuated surface vessel (USV) navigation: signal transmission constraints and malicious maneuvering by obstacle vessels. Continuous signal transmission risks communication loads and system instability. Critically, malicious maneuvering violating the International Regulations for Preventing Collisions at Sea (COLREGs) significantly increases collision risks and threatens maritime safety, which remains underexplored in existing obstacle avoidance research. To balance low communication loads against effective obstacle avoidance, a novel adaptive neural hybrid threshold event-triggered control algorithm is proposed, which is divided into a guidance module and a control module. By combining the dynamic virtual ship (DVS) guidance principle for path following with a modified velocity obstacle (VO) method introducing the quaternion ship domain, a VO-based DVS guidance principle is developed to simultaneously guarantee the execution of path following and obstacle avoidance missions. Furthermore, the proposed control algorithm integrates the adaptive neural approximation technique with the hybrid threshold event-triggered mechanism to achieve high-precision path following and excellent triggering characteristics. Through the Lyapunov analysis, all signals in the closed-loop control system are guaranteed to be semi-globally uniform ultimate bounded (SGUUB). Ultimately, the superiority of the proposed algorithm is verified by simulation experiments.</p>

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Hybrid event-triggered control for anti-malicious maneuvering obstacle avoidance of USVs via a velocity obstacle-based dynamic virtual ship guidance

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Guoqing Zhang,
  • Yu Zhang,
  • Jiqiang Li,
  • Zhihao Li

摘要

This paper focuses on challenges in underactuated surface vessel (USV) navigation: signal transmission constraints and malicious maneuvering by obstacle vessels. Continuous signal transmission risks communication loads and system instability. Critically, malicious maneuvering violating the International Regulations for Preventing Collisions at Sea (COLREGs) significantly increases collision risks and threatens maritime safety, which remains underexplored in existing obstacle avoidance research. To balance low communication loads against effective obstacle avoidance, a novel adaptive neural hybrid threshold event-triggered control algorithm is proposed, which is divided into a guidance module and a control module. By combining the dynamic virtual ship (DVS) guidance principle for path following with a modified velocity obstacle (VO) method introducing the quaternion ship domain, a VO-based DVS guidance principle is developed to simultaneously guarantee the execution of path following and obstacle avoidance missions. Furthermore, the proposed control algorithm integrates the adaptive neural approximation technique with the hybrid threshold event-triggered mechanism to achieve high-precision path following and excellent triggering characteristics. Through the Lyapunov analysis, all signals in the closed-loop control system are guaranteed to be semi-globally uniform ultimate bounded (SGUUB). Ultimately, the superiority of the proposed algorithm is verified by simulation experiments.