Adaptive fixed-time bipartite tracking consensus control for unknown nonlinear multi-agent systems: An information classification mechanism

This paper is concerned with the problem of bipartite tracking consensus for high-order unknown nonlinear multi-agent systems with actuator faults. Unlike the traditional condition that the directed signed graph is structurally balanced, a directed signed graph containing a spanning tree is considered. Besides, the consensus errors are required to satisfy both the prescribed performance and fast convergence (fixed-time). By proposing an information classification mechanism, each agent selectively uses neighbor information such that agents in the system are divided into two styles, which transform the bipartite tracking consensus problem into a general tracking consensus problem. By using neural networks and adaptive technologies to approximate unknown functions, the adaptive fault-tolerant fixed-time consensus controllers are developed. All signals in the system are bounded within a fixed time. Moreover, the bipartite consensus errors satisfy the prescribed performance by selecting appropriately predefined performance functions. Stability analysis and simulation results further verify the effectiveness of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.