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Adaptive ELM neural computing framework with fuzzy PI controller for speed regulation in permanent magnet synchronous motors

Authors
  • Vijay Amirtha Raj, F.1
  • Kamatchi Kannan, V.2
  • 1 RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India , Coimbatore (India)
  • 2 Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India , Sathyamangalam (India)
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
May 06, 2020
Volume
24
Issue
14
Pages
10963–10980
Identifiers
DOI: 10.1007/s00500-020-04994-6
Source
Springer Nature
Keywords
License
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Abstract

In this work, a new adaptive extreme learning machine (ELM) neural network-based fuzzy controller is designed and simulated for implementing speed regulation in a permanent magnet synchronous motor. ELM is a neural model wherein the number of hidden neurons to be placed in the hidden layer is tuned during the process of neural network training itself. A new adaptive ELM model is developed for placing the number of hidden neurons in the hidden layer, and this new adaptive ELM is tuned with artificial bee colony (ABC) algorithm for optimizing its weight parameters and also the number of hidden neurons. Fuzzy proportional–integral (PI) controller is developed in this work in order to eliminate the steady-state error. The new adaptive ELM neural model optimized with ABC algorithm is applied to tune the input parameters of the fuzzy PI controller and also on optimizing the rules and fuzzy membership functions. The optimized adaptive ELM neural network-based fuzzy PI controller is utilized to investigate the speed regulation of permanent magnet synchronous motor (PMSM) in this work. The developed new PI controller with the PMSM is tested for its performance characteristics and to prove its validity is compared with the traditional controller and other heuristic controllers proposed in earlier literature works.

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