| 蔡军,潘锡山.基于自适应迭代学习的多智能体系统编队控制[J].电子测量与仪器学报,2024,38(4):76-84 |
| 基于自适应迭代学习的多智能体系统编队控制 |
| Formation control of multi-agent systems based on adaptive iterative learning |
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| DOI： |
| 中文关键词: 多智能体系统 自适应迭代学习控制 时变参数 多无人机编队系统 |
| 英文关键词:multi-agent systems adaptive iterative learning control time-varying parameters multi-UAV formation systems |
| 基金项目:国家自然科学基金（61906026）项目资助 |
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| 摘要点击次数: 293 |
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| 中文摘要: |
| 针对带未知时变参数的非线性多智能体系统的编队问题，提出一种分布式自适应迭代学习控制策略。首先，通过傅里叶级数对系统的不确定参数进行展开，采用一个收敛级数序列处理傅里叶级数展开产生的截断误差，结合多智能体运行过程中的编队误差推导自适应迭代学习控制律和参数更新律；其次，针对领导者动态对大部分智能体都是未知的情况，设计新的辅助控制来补偿未知动态和避免未知有界干扰；然后，基于李亚普诺夫能量函数证明了在所设计控制律作用下多智能体系统编队误差随着迭代次数的增加在有限时间内趋于0；最后，将该控制策略运用到多无人机编队系统中，并通过搭建半物理实验平台，验证了控制方法的有效性。实验结果表明该控制方法可以确保多智能体快速形成所需编队，并且每个智能体在有限时间内可以精确跟踪期望轨迹。所提方法充分考虑了多智能体系统的参数不确定性以及抗干扰的能力，为实际应用中复杂多智能体系统的精确控制提供了有效的方法。 |
| 英文摘要: |
| A distributed adaptive iterative learning control strategy is proposed for the formation problem of nonlinear multi-agent systems with unknown time-varying parameters. Firstly, the uncertain parameters of the system are expanded through Fourier series, and a convergent series sequence is employed to handle the truncation error resulting from the Fourier series expansion. Combined with the formation error during the operation of the multi-agent system, the adaptive iterative learning control law and parameter update law are derived. Secondly, for scenarios where the dynamics of the leader are unknown to most agents, a new auxiliary control is designed to compensate for the unknown dynamics and avoid unknown bounded interference. Then, based on the Lyapunov energy function, it is proved that the formation error of the multi-agent system tends to be zero within a limited time as the number of iterations increases under the action of the designed control law. Finally, this control strategy is applied to multi-UAV formation systems, and its effectiveness is validated through the construction of a semi-physical experimental platform. Experimental results demonstrate that this control method can ensure rapid formation of the required formation by multiple agents, and each agent can accurately track the desired trajectory within a limited time. The proposed method fully considers the parameter uncertainty and anti-interference ability of multi-agent systems, providing an effective approach for the precise control of complex multi-agent systems in practical applications. |
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