High dynamic control of a flexure fast tool servo using on-line sequential extreme learning machine

Zelong Wu, Hui Tang, Xin Chen, Jian Gao, Yunbo He, Ying Xu, Xun Chen, Suet To, Yangmin Li, Chengqiang Cui

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Flexure-guided fast tool servo (FTS) driven by piezoelectric actuator (PEA) has the advantages of high accuracy and high speed, which makes it has been widely applied in the microstructure surface processing. Unfortunately, PEA has complicated hysteresis nonlinearity, which will greatly reduce the processing accuracy. The common PID and other traditional control methods are hard to handle complex hysteresis nonlinearity issue. As a classic method of intelligent hysteresis modeling, the traditional artificial neural network (TANN) algorithm can model the hysteresis nonlinearity accurately, however, the high-frequency dynamic hysteresis modeling based on TANN is difficult to be achieved on-line. Therefore, a novel on-line sequential extreme learning machine (OS-ELM) modeling method is proposed in this work. A compound control strategy consists of the OS-ELM model and PID feedback (OSEP) controller is proposed. A series of validation experiments are successfully carried out. The parameter identification results show that the training speed of the OS-ELM model is 836 times faster than that of the TANN model, and the identification accuracy is improved by 475 times. The closed-loop control results show that the positioning accuracy with OS-ELM hysteresis compensation is 13 times higher than with TANN model. It proves that the FTS system can achieve a satisfactory performance (stroke: 120μm, average linearity: 0.54%) under high closed-loop bandwidth 200Hz.

Original languageEnglish
Title of host publicationAIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages604-609
Number of pages6
ISBN (Print)9781538618547
DOIs
Publication statusPublished - 30 Aug 2018
Event2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018 - Auckland, New Zealand
Duration: 9 Jul 201812 Jul 2018

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2018-July

Conference

Conference2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018
Country/TerritoryNew Zealand
CityAuckland
Period9/07/1812/07/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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