Particle swarm optimization on designing anticipatory iterative learning controller for positioning accuracy

Yi Cheng Huang, Shih Wen Hsu

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

Abstract

This paper applies a particle swarm optimization (PSO) technique using hybrid terms. This technique is used to adjust the learning gain of the anticipatory iterative learning control (AILC). This research proposes the hybrid PSO cognitive and social components on the updating velocity terms. The optimized AILC's gain facilitates the improvement of the learning process and positioning accuracy. Simulations were conducted, and the results were compared with the fixed gain of ILC, fixed gain of AILC and three lead time of AILC by using PSO method. The learnable error signals through a bandwidth-tuning mechanism adaptively and successfully shaped the new input trajectory. The simulation results validate the effectiveness of the new PSO-AILC for precision accuracy for a one-axis linear motor.

Original languageEnglish
Title of host publication2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages483-486
Number of pages4
ISBN (Electronic)9781509060870
DOIs
Publication statusPublished - 2017 Jun 7
Event3rd International Conference on Control, Automation and Robotics, ICCAR 2017 - Nagoya, Japan
Duration: 2017 Apr 222017 Apr 24

Publication series

Name2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017

Other

Other3rd International Conference on Control, Automation and Robotics, ICCAR 2017
CountryJapan
CityNagoya
Period17-04-2217-04-24

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Optimization
  • Control and Systems Engineering

Cite this

Huang, Y. C., & Hsu, S. W. (2017). Particle swarm optimization on designing anticipatory iterative learning controller for positioning accuracy. In 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017 (pp. 483-486). [7942743] (2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAR.2017.7942743