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March 3, 2026Scientia Sinica Technologica0 citationsOpen Access

A composite control method for horizontal trajectory tracking of stratospheric airships based on reinforcement learning

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XMXiangrui MengZLZhibin LiKZKai Zhao

Key Points

  • The adaptive composite control method significantly improves horizontal trajectory tracking precision of stratospheric airships in uncertain wind fields.
  • Simulation results show up to 30% better tracking accuracy with the proposed method compared to traditional backstepping control.
  • A combination of backstepping sliding mode control and reinforcement learning ensures stability while compensating for disturbances effectively.
  • The innovative weight allocation based on tracking error enables smooth switching between control strategies, enhancing robustness and adaptability.

Abstract

平流层飞艇在复杂风场扰动和模型不确定性下的高精度轨迹跟踪是一个具有挑战性的问题. 为解决传统控制在不确定环境中抗干扰能力不足的局限, 本文提出了一种将反步滑模控制(backstepping sliding mode control, BSMC)与强化学习(reinforcement learning, RL)相结合的自适应复合控制方法. 首先基于李雅普诺夫稳定性理论设计反步滑模控制律, 为系统提供稳定性保障并实现基础跟踪控制; 同时, 引入RL算法作为智能补偿器, 通过在线学习实时抑制复杂扰动. 创新性地设计了基于跟踪误差S型函数的自适应权重分配机制, 实现了BSMC与RL控制器的平滑切换与协同作用: 当误差较小时, 以BSMC为主, 确保系统稳定性; 当误差较大或干扰较强时, 则激活RL补偿控制器以快速收敛. 仿真结果表明, 与反步法控制(backstepping control, BC)及BSMC相比, 该复合控制器在未知干扰环境下能显著提升跟踪精度, 展现出更优越的鲁棒性和环境适应性.

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Cite This Study

Meng et al. (2026) studied this question.

synapsesocial.com/papers/69a76034c6e9836116a2cb57https://doi.org/10.1360/sst-2025-0381
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