An interative learning control scheme using the weighted least-squares method

Keigo Watanabe, Toshio Fukuda, Spyros G. Tzafestas

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.

Original languageEnglish
Pages (from-to)267-284
Number of pages18
JournalJournal of Intelligent & Robotic Systems
Volume4
Issue number3
DOIs
Publication statusPublished - Sep 1 1991
Externally publishedYes

Keywords

  • Iterative learning control
  • Kalman filter
  • impulse response model
  • robot manipulator
  • sparse system structure
  • weighted least-squares method

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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