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

Yi Cheng Huang, Shih Wen Hsu

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面483-486
頁數4
ISBN(電子)9781509060870
DOIs
出版狀態Published - 2017 六月 7
事件3rd International Conference on Control, Automation and Robotics, ICCAR 2017 - Nagoya, Japan
持續時間: 2017 四月 222017 四月 24

出版系列

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

Other

Other3rd International Conference on Control, Automation and Robotics, ICCAR 2017
國家Japan
城市Nagoya
期間17-04-2217-04-24

All Science Journal Classification (ASJC) codes

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

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