Cooperative Control of Multi-Agent Systems With Unknown State-Dependent Controlling Effects

Abstract

This paper investigates the cooperative control problem of uncertain high-order nonlinear multi-agent systems on directed graph with a fixed topology. Each follower is assumed to have an unknown controlling effect which depends on its own state. By the Nussbaum-type gain technique and the function approximation capability of neural networks, a distributed adaptive neural networks-based controller is designed for each follower in the graph such that all followers can asymptotically synchronize the leader with tracking errors being semi-globally uniform ultimate bounded. Analysis of stability and parameter convergence of the proposed algorithm are conducted based on algebraic graph theory and Lyapunov theory. Finally, a example is provided to validate the theoretical results. Note to Practitioners-Many practical applications can be modeled as uncertain high-order nonlinear multi-agent systems, whose node' controlling effects are state-dependent. In most relevant literatures, however, it is often assumed that each node' controlling effects are equal to one. How to cooperative control for the systems has become one main focus of control researches. Therefore, in this paper, an adaptive cooperative control scheme is proposed for such multi-agent systems. Finally, the effectiveness of the control strategies is illustrated via simulation study.