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Observer-Based Adaptive Fuzzy Distributed Control of Non-affine Multi-agent Systems with Input Quantization

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Abstract

In this paper, observer-based adaptive fuzzy distributed controller is proposed for non-affine multi-agent systems with input quantization under a directed graph topology. In contrast to some existing literatures, input quantization is considered in non-affine multi-agent systems, which not only reduce the waste of resources between agents communication, but also can better represent the systems in practical engineering. Then, by applying a Nussbaum function and nonlinear decomposition, the obstacles of unknown control direction cased by non-affine and input quantization are successfully circumvented. On this basis, a state observer based on fuzzy logic systems is constructed to counteract the unmeasurable states and unknown nonlinear dynamics of non-affine multi-agent systems. Through Lyapunov stability theory based on dynamic surface control, it is proved that all signals in multi-agent systems are cooperative semi-global uniform and ultimately bounded (CSUUB). The consistency tracking error and the estimation error converge on a small neighborhood of the origin. Finally, simulation experiment show the effectiveness of the above methods.

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Acknowledgements

Thanks are due to the National Natural Science Foundation of China under Grant Nos. 62173078, 61773105, 61533007, 61873049, 61873053, 61703085 and 61374147 and the Fundamental Research Funds for the Central Universities under Grant No. N182008004 for supporting this research work.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Xin Li, Dakuo He & Qiang Zhang

  2. State Key Laboratory of Synthetical Automation for Process Industries, Shenyang, 110819, China

    Dakuo He

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  1. Xin Li
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  2. Dakuo He
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Correspondence to Dakuo He.

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Li, X., He, D. & Zhang, Q. Observer-Based Adaptive Fuzzy Distributed Control of Non-affine Multi-agent Systems with Input Quantization. Int. J. Fuzzy Syst. 25, 118–135 (2023). https://doi.org/10.1007/s40815-022-01354-4

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