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<p>This article investigates the finite-time leader-following formation control (LFC) problem for multiple unmanned surface vehicles (USVs) subject to disturbances and hybrid irregular (HI) output constraints. Here, the HI output constraint refers to a generalized output constraint that alternates irregularly among unilateral (only upper or lower) constraints, bilateral (both upper and lower) constraints, and free-constraint over time. To handle HI output constraints in multi-USV scenarios, a novel barrier function (BF) is proposed to uniformly transform both bilateral and unilateral constrained systems into unconstrained ones for controller designs. Subsequently, a zero-sum game (ZSG)-based controller is developed for multi-USVs to guarantee robust optimal control (ROC) against the worst-case disturbance without extra anti-disturbance module designs. Furthermore, the identifier-actor-critic reinforcement learning (IAC-RL) framework is designed in the ZSG-based control scheme, where the identifier, critic, and actor are utilized to estimate unknown system dynamics, evaluate system performance, and implement control policy, respectively. Finally, the effectiveness of the proposed control scheme is demonstrated through simulations of the multi-USV model.</p>

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Zero-sum game-based formation control for multi-USVs under hybrid irregular output constraints

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Daocheng Tang,
  • Ning Pang,
  • Xin Wang,
  • Yan Lei

摘要

This article investigates the finite-time leader-following formation control (LFC) problem for multiple unmanned surface vehicles (USVs) subject to disturbances and hybrid irregular (HI) output constraints. Here, the HI output constraint refers to a generalized output constraint that alternates irregularly among unilateral (only upper or lower) constraints, bilateral (both upper and lower) constraints, and free-constraint over time. To handle HI output constraints in multi-USV scenarios, a novel barrier function (BF) is proposed to uniformly transform both bilateral and unilateral constrained systems into unconstrained ones for controller designs. Subsequently, a zero-sum game (ZSG)-based controller is developed for multi-USVs to guarantee robust optimal control (ROC) against the worst-case disturbance without extra anti-disturbance module designs. Furthermore, the identifier-actor-critic reinforcement learning (IAC-RL) framework is designed in the ZSG-based control scheme, where the identifier, critic, and actor are utilized to estimate unknown system dynamics, evaluate system performance, and implement control policy, respectively. Finally, the effectiveness of the proposed control scheme is demonstrated through simulations of the multi-USV model.