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. 2022 Sep 20;22(19):7115.
doi: 10.3390/s22197115.

Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems

Shangyu Sang  1 , Ruikun Zhang  1 , Xue Lin  1
Affiliations

Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems

Shangyu Sang et al. Sensors (Basel). .

Abstract

This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) to solve the bipartite containment problem of MASs. The designed controller only relies on the input and output data of the agent without requiring the model information of MASs. Secondly, we give the convergence condition that the containment error asymptotically converges to zero. The result shows that the output states of all followers will converge to the convex hull formed by the output states of leaders and the symmetric output states of leaders. Finally, the simulation verifies the effectiveness of the proposed method.

Keywords: model-free adaptive iterative learning control; multi-agent systems; signed networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of solving the bipartite containment problem of MASs.
Figure 2
Figure 2
Signed graph.
Figure 3
Figure 3
The output trajectories of MASs.
Figure 4
Figure 4
The containment error.
See this image and copyright information in PMC

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