Iterative Learning Identification for Linear Time-Varying Systems

Nanjun Liu, Andrew Alleyne

Research output: Contribution to journalArticle

Abstract

This brief presents an approach for identifying the parameters of linear time-varying systems that repeat their trajectories. The identification is based on the concept that parameter identification results can be improved by incorporating information learned from previous executions. The learning laws for this iterative learning identification are determined through an optimization framework. The convergence analysis of the algorithm is presented along with the experimental results to demonstrate its effectiveness. The algorithm is demonstrated to be capable of simultaneously estimating rapidly varying parameters and addressing robustness to noise by adopting a time-varying design approach.

Original languageEnglish (US)
Article number7103033
Pages (from-to)310-317
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Volume24
Issue number1
DOIs
StatePublished - Jan 2016

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Keywords

  • Iterative learning control (ILC)
  • linear time-varying (LTV) systems
  • system identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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