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Particle Swarm Optimized Deep Convolutional Neural Sugeno-Takagi Fuzzy PID Controller in Permanent Magnet Synchronous Motor

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Abstract

Permanent magnet synchronous motor (PMSM) is one of the most viable motion control products due to the inherent benefits of low rotor inertia, high efficiency and high-power density in industrial applications. The speed control for PMSM is a significant task. Many researchers carried out their research for improving performance of PMSM through speed control. But, the performance and efficiency of PMSM are reduced due to the external load disturbances and parameter deviation like nonlinear, time-varying, strong coupling of PMSM. In order to address these problems, Particle Swarm Maxpooling Fully Connective Deep Convolutional Neural Learnt Sugeno-Takagi Fuzzy Controller (PSMFCDCNLSTFC) model is introduced. The key objective of PSMFCDCNLSTFC model is to regulate the speed of PMSM for obtaining the highest current value. PSMFCDCNLSTFC model comprises two processes, namely Particle Swarm Weight and Hidden Neuron Optimization process and Maxpooling Fully Connective Deep Convolutional Recurrent Neural Network-based Takagi-Sugeno Fuzzy Controller process. In the former process, weight parameters and number of hidden neurons are optimized to design efficient deep convolutional neural network. In the latter process, four layers are used to regulate the speed of PMSM through Takagi-Sugeno Fuzzy Controller. After that, the soft sign activation function is used to find the minimum mean square error for attaining the rated current value of PMSM. Finally, the performance of PMSM gets improved. The performance of PSMFCDCNLSTFC model is performed with PMSM data and measured in terms of rise time, peak time, peak value, peak overshoot and settling time. The simulation results show that the PSMFCDCNLSTFC model increases the performance of PMSM with higher output current value when compared to state-of-the-art works.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India

    F. Vijay Amirtha Raj

  2. Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India

    V. Kamatchi Kannan

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  1. F. Vijay Amirtha Raj
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  2. V. Kamatchi Kannan
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Correspondence to F. Vijay Amirtha Raj.

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Raj, F.V.A., Kannan, V.K. Particle Swarm Optimized Deep Convolutional Neural Sugeno-Takagi Fuzzy PID Controller in Permanent Magnet Synchronous Motor. Int. J. Fuzzy Syst. 24, 180–201 (2022). https://doi.org/10.1007/s40815-021-01126-6

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  • DOI: https://doi.org/10.1007/s40815-021-01126-6

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