Prescribed performance tracking control for nonlinear multi-agent systems
WANG Tai-sheng
DOU Li-ya
LI Zhi-qing
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.
Keywords：nonlinear multi-agent systemsprescribed performanceconsensus trackingadaptive weighting radial basis function neural network(AW-RBFNN)
Publication Date：2026-01-30
Online Publishing Date：2026-02-05(First online date of this platform, not the publication date of the document)
Pages：11( 79-89 )