Distributed Asymptotic Tracking for Higher-Order Multi-Agent Systems With Unknown Control Directions Under Directed Graphs
Year: 2025
Type: article
Abstract: This paper addresses the distributed tracking problem for higher-order nonlinear multi-agent systems characterized by unknown control directions and uncertain dynamics under directed graphs. A novel adaptive control algorithm is developed using Nussbaum-type functions, nonlinear transformations, and compensating functions to ensure the asymptotic convergence of all agents to the leader while maintaining the boundedness of all closed-loop signals. Unlike previous results, the presented approach does not rely on function approximators, guarantees asymptotic convergence rather than bounded residual errors, and applies to general directed graphs containing a spanning tree. Theoretical analysis, supported by Lyapunov stability theory, establishes the effectiveness of the proposed controller in handling unknown control directions and system uncertainties. Comparative simulation studies involving a network of single-link robots demonstrate the superior performance of the proposed method in achieving precise distributed tracking. The results show improved convergence accuracy and computational efficiency over existing techniques, making this approach a promising solution for distributed control applications in multi-agent systems.
Source: IEEE Access
Authors: Zhihua Zhang, Chaoli Wang, Xuan Cai
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4408017293