Abstract
This article explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs' mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm.
Highlights
Over the past few years, the cooperative control theories of multiagent systems (MASs) have been wildly researched
We provide the coming assumption and lemma
In this work, a data-driven distributed bipartite consensus tracking scheme has been proposed for unknown nonaffine nonlinear discrete-time multi-agent systems (MASs) with fixed and switching topologies
Summary
Over the past few years, the cooperative control theories of multiagent systems (MASs) have been wildly researched. MASs have been already applied to many practical areas [1]–[3], such as vertical tank systems, automated highway systems, autonomous cars, and satellite formation. The distributed algorithm [4], [5] which is one of the significant algorithms in the cooperative control theories can regulate agents to achieve consensus without a central control unit. Ning et al [4] apply the edge-based fixed-time consensus approach and the Hessian matrix to formulate a distributed protocol, which can successfully guarantee the distributed optimization of MASs under both fixed and switching communication topologies. An effective control protocol of the second-order MASs is proposed in [5] to perform the formation task and maintain predictive performance
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