Optimizing learning convergence speed and converged error for precision motion control

Douglas A. Bristow, Andrew G. Alleyne, Marina Tharayil

Research output: Contribution to journalArticlepeer-review

Abstract

This brief paper considers iterative learning control (ILC) for precision motion control (PMC) applications. This work develops a methodology to design a low pass filter, called the Q-filter, that is used to limit the bandwidth of the ILC to prevent the propagation of high frequencies in the learning. A time-varying bandwidth Q-filter is considered because PMC reference trajectories can exhibit rapid changes in acceleration that may require high bandwidth for short periods of time. Time-frequency analysis of the initial error signal is used to generate a shape function for the bandwidth profile. Key parameters of the bandwidth profile are numerically optimized to obtain the best tradeoff in converged error and convergence speed. Simulation and experimental results for a permanent-magnet linear motor are included. Results show that the optimal time-varying Q-filter bandwidth provides faster convergence to lower error than the optimal time-invariant bandwidth.

Original languageEnglish (US)
Pages (from-to)545011-545018
Number of pages8
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume130
Issue number5
DOIs
StatePublished - Sep 1 2008

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Optimizing learning convergence speed and converged error for precision motion control'. Together they form a unique fingerprint.

Cite this