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Distributed adaptive neural network consensus for a class of uncertain nonaffine nonlinear multi-agent systems

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Abstract

This paper considers the distributed adaptive neural consensus tracking control problem for a class of uncertain nonaffine nonlinear multi-agent systems. By making use of the Taylor expansion technique, the nonaffine nonlinear control input of each subsystem is successfully separated under a weaker decoupling condition, and then, the distributed adaptive control is developed via neural networks (NNs) technique. By introducing the compensation adaptive laws with positive time-varying integrable functions to effectively handle the disturbances and the NN approximation errors in backstepping design process, a new distributed adaptive neural controller is constructed by means of the local output tracking error information of neighborhood agents. It can be proved that all the subsystem outputs asymptotically track to a desired reference trajectory. The efficiency of the established control strategy is demonstrated by the simulation experiment.

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Acknowledgements

This work of J.H. Park and Z. Yang was supported by the National Natural Science Foundation of China under Grant 11971081, and the Fundamental and Frontier Research Project of Chongqing under Grant cstc2018jcyjAX0144. Also, the work of L. Wu was supported in part by the National Natural Science Foundation of China (Grant Nos. 61673098, 61773221, 61903238, 61773013 and U173110085), the Natural Science Foundation of Liaoning Province of China (Grant No. 20180551190), and the Scientific Research Foundation of Liaoning Educational Committee of China (Grant No. 2017LNZD05).

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Authors and Affiliations

  1. School of Science, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, People’s Republic of China

    Li-Bing Wu

  2. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan, 38541, Republic of Korea

    Li-Bing Wu & Ju H. Park

  3. School of Mathematical College, Chongqing Normal University, Chongqing, 401331, People’s Republic of China

    Ju H. Park & Zhichun Yang

  4. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, People’s Republic of China

    Xiang-Peng Xie

  5. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, Shandong, People’s Republic of China

    Yu-Wei Ren

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  1. Li-Bing Wu
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Correspondence to Ju H. Park.

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Wu, LB., Park, J.H., Xie, XP. et al. Distributed adaptive neural network consensus for a class of uncertain nonaffine nonlinear multi-agent systems. Nonlinear Dyn 100, 1243–1255 (2020). https://doi.org/10.1007/s11071-020-05599-2

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