控制理论与应用2026，Vol.43Issue(1)：79-89,11.DOI:10.7641/CTA.2025.50095
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
Abstract
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.An error 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.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)Key words
nonlinear multi-agent systems/prescribed performance/consensus tracking/adaptive weighting radial basis function neural network(AW-RBFNN)引用本文复制引用
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):79-89,11.基金项目
国家自然科学基金项目(62103031)资助.Supported by the National Natural Science Foundation of China(62103031). （62103031）
