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Fuzzy adaptive single neuron NN control of brushless DC motor

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Abstract

Inherently, the brushless DC motor (BLDCM) is a nonlinear plant. So, it is hard to get a good performance by using the conventional PI controller for the speed control of BLDCM. In this paper, a fuzzy adaptive single neuron neural networks (NN) controller for BLDCM is developed. The fuzzy logic system (FLS) is adopted to adjust the parameter K of single neuron NN controller online. By this way, performance of the system can be improved. Performances of the proposed fuzzy adaptive single neuron NN controller are compared with the performances of conventional PI controller and normal single neuron NN controller. The experimental results demonstrate that a good control performance is achieved. The using of fuzzy adaptive single neuron NN makes the drive system robust, accurate, and insensitive to parameter variations.

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Acknowledgments

This work is supported by Natural Science Foundation of National Natural Science Foundation of China (50705062). Tianjin Research Program of Application Foundation and Advanced Technology (11JCYBJC07100). Innovation Foundation of Tianjin University (2010XJ-0174). New Teacher Fund for Doctor Station, the Ministry of Education (20100032120079).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China

    Jie Xiu

  2. School of Mechanical Engineering, Tianjin University, Tianjin, 300072, China

    Shiyu Wang

  3. Department of Fundamental Subject, Tianjin Institute of Urban Construction, Tianjin, 300384, China

    Yan Xiu

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  1. Jie Xiu
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  2. Shiyu Wang
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  3. Yan Xiu
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Correspondence to Jie Xiu.

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Xiu, J., Wang, S. & Xiu, Y. Fuzzy adaptive single neuron NN control of brushless DC motor. Neural Comput & Applic 22, 607–613 (2013). https://doi.org/10.1007/s00521-011-0717-0

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  • DOI: https://doi.org/10.1007/s00521-011-0717-0

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