Tuning of Servo Drive Controller Based on Boosted Tree Model and Particle Swarm Optimization

Chi Wen Chen, Lien Kai Chang, Yi Ting Liao, Chun Hui Chung, Wei Chih Su, Kuo Shen Chen, Mi Ching Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a gain tuning method for servo drives that combines machine learning model (LightGBM) and optimization algorithm (PSO). The LightGBM model can predict the response characteristics of the servo system under different gain parameters of the servo drive. Then, the PSO will tuning the gain parameters to obtain the best performance. A new performance criterion for evaluating the servo drive is proposed. It consists of several time-domain position response characteristics, such as percentage of overshoot and settling time etc., and the servo drive performance can be optimized by utilizing those characteristics. The experimental result shows that the appropriate gain parameters can be obtained through this proposed AI tuning method.

Original languageEnglish
Title of host publication23rd International Conference on Electrical Machines and Systems, ICEMS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-110
Number of pages4
ISBN (Electronic)9784886864192
DOIs
Publication statusPublished - 2020 Nov 24
Event23rd International Conference on Electrical Machines and Systems, ICEMS 2020 - Hamamatsu, Japan
Duration: 2020 Nov 242020 Nov 27

Publication series

Name23rd International Conference on Electrical Machines and Systems, ICEMS 2020

Conference

Conference23rd International Conference on Electrical Machines and Systems, ICEMS 2020
CountryJapan
CityHamamatsu
Period20-11-2420-11-27

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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