Anticipatory iterative learning control for linear motor positioning accuracy by use of hybrid particle swarm optimization

Yi-Cheng Huang, Shih Wen Hsu, Ming You Ma

Research output: Contribution to journalArticle

Abstract

The conventional Iterative Learning Control (ILC) process can potentially excite rich frequency contents based on past error history and injects them into the updated learning process. Nevertheless, the learnable error signals should be extracted, and the non-learnable error signals should be separated by adaptive bandwidth filter before the updated command is injected into next repetition. This paper proposes a new particle swarm optimization (PSO) algorithm by combining several hybrid terms adopted from literatures for the new synergy properties of local and global searches based on cognitive and social terms. This algorithm is used to adjust the three proportional-integral-derivative controller gains, the Anticipatory Iterative Learning Control (AILC) learning gain, and the cutoff bandwidth of the Butterworth filter associated with AILC. Developed hybrid PSO algorithm is devised for the updating velocity terms. The new synthesis AILC control law with adaptive learning gain, bandwidth-tuning filter and PID control gains were optimized by PSO and facilitated the improvement of the learning process and positioning accuracy. Numerical simulations were conducted and compared with the literature's P-type AILC for a linear synchronous motor. The tracking error through the adaptive learning processes was reduced successfully by shaping a new updated input trajectory at every repetition. The experimental results confirm the effectiveness of the new PSO-AILC for ultra-fine positioning in a one-axis linear motor.

Original languageEnglish
Pages (from-to)897-910
Number of pages14
JournalJournal of Intelligent and Fuzzy Systems
Volume36
Issue number2
DOIs
Publication statusPublished - 2019 Jan 1

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Linear Motor
Iterative Learning Control
Hybrid Optimization
Linear motors
Particle swarm optimization (PSO)
Positioning
Particle Swarm Optimization
Learning Process
Adaptive Learning
Bandwidth
Filter
Particle Swarm Optimization Algorithm
Term
Adaptive Processes
Butterworth filters
PID Control
Global Search
Synergy
Three term control systems
Local Search

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

Cite this

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abstract = "The conventional Iterative Learning Control (ILC) process can potentially excite rich frequency contents based on past error history and injects them into the updated learning process. Nevertheless, the learnable error signals should be extracted, and the non-learnable error signals should be separated by adaptive bandwidth filter before the updated command is injected into next repetition. This paper proposes a new particle swarm optimization (PSO) algorithm by combining several hybrid terms adopted from literatures for the new synergy properties of local and global searches based on cognitive and social terms. This algorithm is used to adjust the three proportional-integral-derivative controller gains, the Anticipatory Iterative Learning Control (AILC) learning gain, and the cutoff bandwidth of the Butterworth filter associated with AILC. Developed hybrid PSO algorithm is devised for the updating velocity terms. The new synthesis AILC control law with adaptive learning gain, bandwidth-tuning filter and PID control gains were optimized by PSO and facilitated the improvement of the learning process and positioning accuracy. Numerical simulations were conducted and compared with the literature's P-type AILC for a linear synchronous motor. The tracking error through the adaptive learning processes was reduced successfully by shaping a new updated input trajectory at every repetition. The experimental results confirm the effectiveness of the new PSO-AILC for ultra-fine positioning in a one-axis linear motor.",
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Anticipatory iterative learning control for linear motor positioning accuracy by use of hybrid particle swarm optimization. / Huang, Yi-Cheng; Hsu, Shih Wen; Ma, Ming You.

In: Journal of Intelligent and Fuzzy Systems, Vol. 36, No. 2, 01.01.2019, p. 897-910.

Research output: Contribution to journalArticle

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