Adaptive Distributed Consensus Control for a Class of Heterogeneous and Uncertain Nonlinear Multi-Agent Systems

This paper has been devoted to the design of a distributed consensus control for a class of uncertain nonlinear multi-agent systems in the strict-feedback form. The communication between the agents has been described by a directed graph. Radial-basis function neural networks have been used for the approximation of the uncertain and heterogeneous dynamics of the followers as well as the effect of the neighbors of each agent in the multi-agent system. Then, the proposed scheme based on the dynamic surface control approach has been presented. Stability analysis of the closed-loop system shows that all the signals of the closed-loop system are uniformly ultimately bounded. The proposed scheme solves the consensus problem in the multi-agent systems with uncertain dynamics and avoids the "explosion of complexity" problem. The simulation results of the proposed approach are presented on a group of single-link robots with uncertain dynamics including four followers and one leader. The presented results verify the effectiveness of the proposed method.