Composite Consensus Control and Cooperative Adaptive Learning
Date of Original Version
This paper considers the problem of composite consensus control and cooperative adaptive learning of a class of linear uncertain multi-agent systems (MASs). The objective is to jointly achieve leader-following consensus tracking and accurate identification of unknown system parameters for all follower agents. A new cooperative adaptive consensus control protocol is proposed, which consists of a discontinuous nonlinear state-feedback control law and a series of filters for cooperative adaptation. Attractiveness of this new protocol lies in its utilization of not only relative plant state information but also relative estimate parameter information. The consensus and learning performance is rigorously analyzed using Lyapunov function approach. It is shown that exponential convergence of both consensus tracking errors to zero and adaptation parameters to their true values can be achieved simultaneously under a mild cooperative finite-time excitation (cFTE) condition. This cFTE condition significantly relaxes many existing excitation conditions (e.g., persistent excitation) for exponential parameter convergence in adaptive control systems. A numerical example is used to demonstrate the proposed approach.
Publication Title, e.g., Journal
Proceedings of the IEEE Conference on Decision and Control
Yuan, Chengzhi, Nan Xue, Wei Zeng, and Cong Wang. "Composite Consensus Control and Cooperative Adaptive Learning." Proceedings of the IEEE Conference on Decision and Control 2018-December, (2019): 1409-1414. doi: 10.1109/CDC.2018.8619263.