Design and Implementation of Fuzzy-PI Controllers for PMSM Based on Multi-Objective Optimization Algorithms

I. Hsi Kao, Kuna Chung Lu, Jau Woei Perng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Various intelligent algorithms have been applied to our daily lives, such as fuzzy theory, neural networks, and machine learning. These methods are widely used for solving many real-world problems; however, these algorithms also exhibit deficiencies and limitations. This paper introduces the recently improved algorithm, known as multi-objective particle swarm optimization, based on decomposition and dominance (D^2 MOPSO) in order to design the permanent magnet synchronous motor (PMSM) fuzzy controller for different objects. This means that the user can easily change the customized controller, according to their requirements. Furthermore, this paper compares the final decision of the controller parameter with other algorithms: The multiobjective particle swarm optimization with crowding distance (MOPSO-CD), and nondominated sorting genetic algorithm II (NSGA-II). The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software MATLAB. Finally, we implement the fuzzy controller in an embedded system (DSP28069) to demonstrate that our design matches the reality system response and meets the user's demands with ease.

Original languageEnglish
Title of host publication2017 5th International Conference on Mechanical, Automotive and Materials Engineering, CMAME 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-289
Number of pages5
ISBN (Electronic)9781538604311
DOIs
StatePublished - 16 Nov 2018
Event5th International Conference on Mechanical, Automotive and Materials Engineering, CMAME 2017 - Guangzhou, China
Duration: 1 Aug 20173 Aug 2017

Publication series

Name2017 5th International Conference on Mechanical, Automotive and Materials Engineering, CMAME 2017

Conference

Conference5th International Conference on Mechanical, Automotive and Materials Engineering, CMAME 2017
Country/TerritoryChina
CityGuangzhou
Period1/08/173/08/17

Keywords

  • MOPSO
  • NSGA-II
  • Nondominated sorting genetic algorithm
  • fuzzy control
  • multi-objective optimization algorithm
  • multi-objective particle swarm optimization

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