@inproceedings{90ec14f66c98443597babfab36798694,
title = "Tuning of Servo Drive Controller Based on Boosted Tree Model and Particle Swarm Optimization",
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. ",
author = "Chen, {Chi Wen} and Chang, {Lien Kai} and Liao, {Yi Ting} and Chung, {Chun Hui} and Su, {Wei Chih} and Chen, {Kuo Shen} and Tsai, {Mi Ching}",
note = "Funding Information: This work was supported by the Ministry of Science and Technology, R.O.C. [grant number MOST 108-2622-8-006-014]. Publisher Copyright: {\textcopyright} 2020 The Institute of Electrical Engineers of Japan.; 23rd International Conference on Electrical Machines and Systems, ICEMS 2020 ; Conference date: 24-11-2020 Through 27-11-2020",
year = "2020",
month = nov,
day = "24",
doi = "10.23919/ICEMS50442.2020.9291203",
language = "English",
series = "23rd International Conference on Electrical Machines and Systems, ICEMS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "107--110",
booktitle = "23rd International Conference on Electrical Machines and Systems, ICEMS 2020",
address = "United States",
}