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Coordination control of uncertain topological high-order multi-agent systems: distributed fuzzy adaptive iterative learning approach

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Abstract

This paper demonstrates that the method of T–S fuzzy model can be used to describe the uncertain topological structure for high-order linearly parameterized multi-agent systems (MAS). The dynamic of the leader is only available to a portion of the follower agents; thus, we present a novel distributed adaptive iterative learning control (AILC) protocol without using any global information to deal with the consensus problem of MAS under initial-state learning condition. It is proved that the proposed control protocol ensures all the internal signals in the multi-agent system are bounded, and the follower agents track the leader exactly on the finite time interval [0, T]; a sufficient condition is obtained for the exactly consensus result of the multi-agent system by choosing the appropriate composite energy function. Extensions to the formation control of multi-agent systems are also given. In the end, illustrative examples are shown to verify the availability of the proposed AILC scheme.

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Acknowledgements

This study was funded by the National Nature Science Foundation of China under Grant 61573013, 61603286.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710126, People’s Republic of China

    Hui Wu & Junmin Li

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  1. Hui Wu
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  2. Junmin Li
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Correspondence to Junmin Li.

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Wu, H., Li, J. Coordination control of uncertain topological high-order multi-agent systems: distributed fuzzy adaptive iterative learning approach. Soft Comput 23, 6183–6196 (2019). https://doi.org/10.1007/s00500-018-3271-1

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