引用本文:王太生,窦立亚,李智卿.预设性能非线性多智能体跟踪控制[J].控制理论与应用,2026,43(1):79~89.[点击复制]
WANG Tai-sheng,DOU Li-ya,LI Zhi-qing.Prescribed performance tracking control for nonlinear multi-agent systems[J].Control Theory & Applications,2026,43(1):79~89.[点击复制]
预设性能非线性多智能体跟踪控制
Prescribed performance tracking control for nonlinear multi-agent systems
摘要点击 221  全文点击 28  投稿时间：2025-03-12  修订日期：2025-12-23
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DOI编号  10.7641/CTA.2025.50095
  2026,43(1):79-89
中文关键词  非线性多智能体  预设性能  一致性跟踪  自适应权重径向基函数神经网络(AW-RBFNN)
英文关键词  nonlinear multi-agent systems  prescribed performance  consensus tracking  adaptive weighting radial basis function neural network (AW-RBFNN)
基金项目  国家自然科学基金项目(62103031)资助.
作者单位E-mail
王太生 北京化工大学信息科学与技术学院 wts16622905225@163.com 
窦立亚* 北京化工大学信息科学与技术学院 liyadou@mail.buct.edu.cn 
李智卿 北京化工大学信息科学与技术学院  
中文摘要
      本文研究了非线性多智能体的预设性能一致性跟踪控制问题,与目前大部分非线性多智能体模型不同,考 虑各个智能体状态可能受到的未知外部有界干扰,并且多智能体系统的状态无法直接测量,非线性函数完全未知. 首先,通过引入一种误差转换函数,将具有预定义跟踪误差约束的非线性多智能体系统转换为不受约束但具有期望 性能特征的非线性系统,提出了一种新的自适应权重径向基函数神经网络(AW-RBFNN)系统,用于处理多智能体系 统模型中的未知非线性函数.此外,引入状态观测器用于估计未知的状态变量,并设计了基于AW-RBFNN系统和状 态观测器的控制律,通过李雅普诺夫和预设性能稳定理论分析,证明了闭环多智能体系统的一致性跟踪误差保持在 预定义区域,并且所有闭环信号都保持一致有界,非线性多智能体系统实现了满足预设性能的跟踪控制.通过数值 仿真例子和建模为实际机械系统的非线性多智能体系统跟踪控制实例,将本文基于AW-RBFNN方法与基于多维泰 勒网(MTN)方法进行对比实验,验证了本文基于AW-RBFNN的预设性能协同跟踪控制方法的有效性.
英文摘要
      This paper studies the prescribed performance consensus tracking control problem for nonlinear multi-agent systems (MASs). Unlike most existing nonlinear MAS models, this approach considers unknown external bounded dis turbances affecting individual agent states, with the MAS states being unmeasurable directly and the nonlinear functions completely unknown. Anerror transformation function is introduced to convert the nonlinear MAS with predefined tracking error constraints into an unconstrained nonlinear system exhibiting desired performance characteristics. A novel adaptive weighting radial basis function neural network (AW-RBFNN) system is proposed to address unknown nonlinear functions in the MAS model. Additionally, a state observer is employed to estimate unmeasurable state variables, and a control law is designed based on the AW-RBFNN system and state observer. Through Lyapunov stability theory and prescribed per formance stability analysis, it is demonstrated that the consensus tracking error converges to a predefined region while all closed-loop signals remain uniformly ultimately bounded, enabling the nonlinear MAS to achieve prescribed-performance satisfying tracking control. The effectiveness of the prescribed performance collaborative tracking control method based on AW-RBFNN is verified by comparing with the multi-dimensional Taylor net (MTN) based method through an numerical simulation example and tracking control examples modeled of nonlinear agents as actual mechanical systems.