Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines

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Abstract

This paper deals with Takagi-Sugeno-Kang (TSK) type fuzzy PID controllers in the context of nonlinear dynamic systems. The design framework for tuning the controller gains depends on particle swarm optimization (PSO), assuming the nonlinear system approximated by an artificial neural network, leading to an overall robust control methodology based on TSK fuzzy PID control. Obtained data and information from simulations and experimental tests considering a nonlinear dynamic process including a DC electrical machine confirm the effectiveness of the control strategy.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-316
Number of pages8
ISBN (Electronic)9781728142180
DOIs
Publication statusPublished - Jul 2020
Event14th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 - Virtual, Online, Portugal
Duration: 8 Jul 202010 Jul 2020

Conference

Conference14th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020
CountryPortugal
CityVirtual, Online
Period8/07/2010/07/20

Keywords

  • DC Electrical Machines
  • Nonlinear Neural Models
  • Nonlinear Systems
  • Particle Swarm Optimization
  • TSK Fuzzy PID Control

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