Profile tracking control of hybrid stepping motors using a learning method

W. D. Chen, G. Feng, Kai Leung Yung

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

Abstract

Servo control of the hybrid stepping motor is complicated due to its highly nonlinear torque-current-position characteristics, especially under low operating speeds. The purpose of this paper is to develop simple and efficient control algorithms for the high-precision tracking control of hybrid stepping motors. The principles of learning control have been exploited to minimize the motor's torque ripple that is periodic and nonlinear in the system states with specific emphasis on low-speed conditions. The proposed algorithm utilizes a fixed PI feedback controller to stabilize the servomotor and the feedforward learning controller to compensate for the effect of the torque ripple and other disturbances for improved tracking accuracy. The stability and convergence performance of the learning control scheme is discussed. It has been revealed that all the error signals in the learning control system are bounded and the motion trajectory converges to the desired one asymptotically. The experimental results demonstrate the effectiveness and performance of the proposed algorithm.
Original languageEnglish
Title of host publicationProceedings - 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, RISSP 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages208-213
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2003
EventIEEE International Conference on Robotics, Intelligent Systems and Signal Processing, RISSP 2003 - Changsha, Hunan, China
Duration: 8 Oct 200313 Oct 2003

Conference

ConferenceIEEE International Conference on Robotics, Intelligent Systems and Signal Processing, RISSP 2003
Country/TerritoryChina
CityChangsha, Hunan
Period8/10/0313/10/03

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Artificial Intelligence

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