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<p>This paper investigates the trajectory tracking for autonomous surface vehicles controlled over unreliable satellite channels. To address the adaptability limitations of fixed communication parameters, a communication-control co-design model predictive control framework is proposed. The framework jointly optimizes the control sequence and communication parameters by minimizing a unified cost function to balance control performance and communication energy. Moreover, a sufficient condition for stochastic stability is derived, which provides an explicit state-dependent upper bound on the packet error rate. This dynamic bound is integrated into the optimization as a constraint to ensure robustness against bounded disturbances. Additionally, to reduce the computational load of online stability assessment, an event-triggered mechanism is used. Comparative simulations demonstrate that the proposed method reduces communication energy consumption by up to 48.3% compared to fixed-parameter approaches, and it reduces the cumulative tracking error by up to 13.6% compared to standard NMPC under equivalent energy constraints.</p>

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Stochastic stability constrained model predictive control for trajectory tracking of autonomous surface vehicle under unreliable channels

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Haoran Dai,
  • Haoyu Zhou,
  • Yongbao Wu,
  • Jian Liu,
  • Lei Xue

摘要

This paper investigates the trajectory tracking for autonomous surface vehicles controlled over unreliable satellite channels. To address the adaptability limitations of fixed communication parameters, a communication-control co-design model predictive control framework is proposed. The framework jointly optimizes the control sequence and communication parameters by minimizing a unified cost function to balance control performance and communication energy. Moreover, a sufficient condition for stochastic stability is derived, which provides an explicit state-dependent upper bound on the packet error rate. This dynamic bound is integrated into the optimization as a constraint to ensure robustness against bounded disturbances. Additionally, to reduce the computational load of online stability assessment, an event-triggered mechanism is used. Comparative simulations demonstrate that the proposed method reduces communication energy consumption by up to 48.3% compared to fixed-parameter approaches, and it reduces the cumulative tracking error by up to 13.6% compared to standard NMPC under equivalent energy constraints.