Observer-based robust AILC for robotic system tracking problem

Chieh-Li Chen, Kai Sheng Li

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

A dynamic observer-based control scheme for trajectory tracking of robotic systems with parametric uncertainty is proposed in this paper. In the proposed control scheme, an integral action is augmented into the robotic system to avoid the chattering of iterative learning control without decreasing control accuracy. The learning control scheme uses a linear observer which is model independent. The proposed approach does not require any prior knowledge of parameter values on robot dynamics. The learning control algorithm includes a feedback controller and an adaptive learning term, to which a hybrid adaptive updating law is applied. The hybrid adaptive updating law updates the learning control parameter for each iterative operation and modifies the learning control parameter to compensate for closed-loop system uncertainty and to overcome external disturbances in the time domain. A Lyapunov-like energy function is utilized to analyze learning convergence and to derive the control algorithm. Simulation results are provided to illustrate the performance of the proposed dynamic learning control algorithm.

Original languageEnglish
Pages (from-to)483-491
Number of pages9
JournalJournal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao
Volume30
Issue number6
Publication statusPublished - 2009 Dec 1

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

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