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Cooperative learning from adaptive neural control for a group of strict-feedback systems

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Abstract

This paper investigates the distributed cooperative learning (DCL) from adaptive neural network (NN) control for a group of strict-feedback systems, where the structure of all strict-feedback systems is identical. In order to achieve DCL easily, the system transformation method is employed for the strict-feedback systems. For an agent, only one radial basis function NN is used to approximate the lumped uncertainty in control design. Then the output tracking performance of all strict-feedback systems is guaranteed. What’s more, we prove that weights of all NNs in a multi-agent system converge to a small neighborhood around their common optimal value if the topology of the multi-agent system is connected and undirected. Thus, the approximation domain of all NNs is enlarged. Further, the previous learned NNs are used to improve the control performance. Finally, we provide two examples to demonstrate the effectiveness of the proposed scheme.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61966026, 61941304 and 61673014, 62163030, and in part by Natural Science Foundation of Inner Mongolia under Grants 2020BS06004 and 2019BS06006.

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Authors and Affiliations

  1. College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021, China

    Fei Gao, Fengshan Bai, Zhi Weng & Xitai Na

  2. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Jing Li

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  1. Fei Gao
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Correspondence to Fei Gao.

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Gao, F., Bai, F., Weng, Z. et al. Cooperative learning from adaptive neural control for a group of strict-feedback systems. Neural Comput & Applic 34, 14435–14449 (2022). https://doi.org/10.1007/s00521-022-07239-9

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  • DOI: https://doi.org/10.1007/s00521-022-07239-9

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