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Adaptive iterative learning control based on particle swarm optimization

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Abstract

The convergence rate of the traditional iterative learning control algorithm is slow, and the application range is narrow. This paper mainly focuses on the optimization of iterative learning control algorithm. It improves the traditional iterative learning control algorithm, and improves the iterative learning control algorithm through particle swarm adaptive algorithm. An adaptive optimization iterative learning control algorithm with particle swarm is proposed. Not only can the convergence speed of the algorithm be improved, but also the uncertainty of the model in the algorithm is solved.

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Acknowledgements

This research was supported by the Natural Science Foundation of Gansu Province No. 1610RJZA024.

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Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Qun Gu

  2. School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Xiaohong Hao

Authors
  1. Qun Gu
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  2. Xiaohong Hao
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Correspondence to Qun Gu.

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Gu, Q., Hao, X. Adaptive iterative learning control based on particle swarm optimization. J Supercomput 76, 3615–3622 (2020). https://doi.org/10.1007/s11227-018-2566-4

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  • DOI: https://doi.org/10.1007/s11227-018-2566-4

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