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. 2024 Jan-Mar;107(1):368504241227620.
doi: 10.1177/00368504241227620.

Data-based bipartite formation control for multi-agent systems with communication constraints

Juqin Wang  1 , Huarong Zhao  2 , Hongnian Yu  3 , Ruitian Yang  4 , Jiehao Li  5
Affiliations

Data-based bipartite formation control for multi-agent systems with communication constraints

Juqin Wang et al. Sci Prog. 2024 Jan-Mar.

Abstract

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.

Keywords: Data-driven control; bipartite formation; data quantization; multi-agent systems; sensor saturation.

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Conflict of interest statement

Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest for the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Communication topologies of MASs.
Figure 2.
Figure 2.
Diagram of agent i with the QDBFC scheme.
Figure 3.
Figure 3.
Tracking performances of multi-agent systems (MASs) in Example 1. (a) θ=0.9 ; (b) θ=0.2 ; (c) θ=0.2 and suffering saturation.
Figure 4.
Figure 4.
Tracking errors of multi-agent systems (MASs) in Example 1. (a) θ=0.9 ; (b) θ=0.2 ; (c) θ=0.2 and suffering saturation.
Figure 5.
Figure 5.
The outputs of the multi-agent systems (MASs) with different methods. (a) The proposed QDBFC method; (b) The existing method
Figure 6.
Figure 6.
The inputs of the multi-agent systems (MASs) with different methods. (a) The proposed QDBFC method; (b) The existing method.
Figure 7.
Figure 7.
Tracking test of multi-agent systems (MASs) with switching topologies in Example 2. (a) Tracking performance of MASs; (b) Tracking errors of MASs
Figure 8.
Figure 8.
Hardware test platform with five SRVO2.
Figure 9.
Figure 9.
Communication topology and speed of five SRV02. (a) Topology of five SRV02; (b) The proposed scheme; (c) The existing scheme
See this image and copyright information in PMC

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