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Grey Wolf Optimizer in Design Process of Stable Neural Controller – Theoretical Background and Experiment

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Advanced, Contemporary Control

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1196))

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Abstract

This article deals with an adaptive neural controller applied for a nonlinear plant with time-varying parameters. The structure of the controller is based on Radial Basis Function Neural Network. The output part of the controller (weights) is modified in several iterations of the control structure. In this application, the coefficients of the Gaussian functions are constant (it means the centers and width). The relevance of proper selection of those values is presented in tests performed for a real plant (an electrical drive). Moreover, for optimization of this part of the controller the metaheuristic – Grey Wolf Optimizer – algorithm was applied. The centers were selected in a clustering process. The synthesis of the controller includes stability analysis (using the Lyapunov method). The content of this article can be divided into two basic parts, the first shows theoretical considerations and the second is related to the experimental tests of the analyzed neural controller (executed in a laboratory, for the drive with 0.5 kW nominal power, using dSPACE card).

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Authors and Affiliations

  1. Wroclaw University of Science and Technology, Wroclaw, Poland

    Marcin Kaminski

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  1. Marcin Kaminski
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Correspondence to Marcin Kaminski .

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Editors and Affiliations

  1. Institute of Automatic Control, Lodz University of Technology, Lodz, Poland

    Andrzej Bartoszewicz

  2. Institute of Automatic Control, Lodz University of Technology, Lodz, Poland

    Jacek Kabziński

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Kaminski, M. (2020). Grey Wolf Optimizer in Design Process of Stable Neural Controller – Theoretical Background and Experiment. In: Bartoszewicz, A., Kabziński, J., Kacprzyk, J. (eds) Advanced, Contemporary Control. Advances in Intelligent Systems and Computing, vol 1196. Springer, Cham. https://doi.org/10.1007/978-3-030-50936-1_59

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