Hierarchical decomposition based distributed adaptive control for output consensus tracking of uncertain nonlinear systems

In this paper, we aim to design distributed adaptive controllers for output consensus tracking of multiple nonlinear subsystems with intrinsic mismatched unknown parameters. The graph representing the communication status among subsystems is assumed to have directed and fixed topology. Only a small percentage of the subsystems can obtain the desired trajectory information, which is regarded as a virtual leader node added to the original communication graph. We first split the communication graph into a hierarchical structure according to the shortest possible path of each subsystem originated from the virtual leader. Then local adaptive controllers for subsystems in different layers can be designed in a sequential order. By introducing the estimates of the uncertainties of its neighbors located in the upper layer into the local controller of a subsystem, the transmission of parameter estimates among connected subsystems is avoided. It is proved that output consensus tracking of the overall system can be achieved asymptotically and all closed-loop signals are ensured bounded. Simulation results show the effectiveness of our scheme.