Cooperative Output Regulation of Unknown Linear Multiagent Systems: When Deadbeat Control Meets Data-Driven Framework
Engang Tian, Ganghui Zhai, Dong Liang, Jinliang Liu
Abstract
In this article, the cooperative output regulation problem (CORP) for unknown linear multiagent systems (MASs) is studied on the basis of a data-driven approach. Different from existing literature, first, the proposed method does not require prior knowledge of the system matrices and the leader system where the control gains are designed using input and state data. Next, the solution to the unknown regulator equations is derived using the data informativity approach. Then, to speed up the convergence rate, a distributed deadbeat control law is proposed for the MASs. For the purpose, the leader's system matrix is identified according to the measured data, and then a distributed observer is employed to estimate the state of the leader system in a distributed manner. Furthermore, the CORP is converted to a stabilization problem by using the data-based solution of the regulator equations. On the basis of the distributed observer, a data-driven deadbeat control law is designed in the context where the system matrices of the plant are completely unknown. It is guaranteed that the tracking error for each agent can converge to the origin asymptotically. Finally, an example is conducted to illustrate the effectiveness of the proposed data-based framework.