Iterative-learning-based tracking control of a two-wheeled mobile robot with model uncertainties and unknown periodic disturbances

Lin Yu, Junlin Xiong*, Min Xie

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Citations (Scopus)

Abstract

In this paper, we develop an adaptive iterative learning approach to investigate the trajectory tracking control issue for a class of two-wheeled mobile robots subject to model uncertainties and unknown disturbances. First, we derive the nonlinear velocity error dynamics. Then a parameterization-based adaptive iterative learning control scheme is adopted to achieve precise tracking, along with logic-based update law for the estimated period and bound of the disturbance. Moreover, the boundness of all the closed-loop signals is rigorously analyzed based on the Lyapunov stability theory to provide the theoretical foundation for the proposed method. The experimental results show the efficacy and viability of our results.

© 2024 The Franklin Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Original languageEnglish
Article number106962
JournalJournal of the Franklin Institute
Volume361
Issue number11
Online published27 May 2024
DOIs
Publication statusPublished - Jul 2024

Funding

This work was partially supported by the National Natural Science Foundation of China under Grant 61773357/62273320 , and partially by the National Natural Science Foundation of China ( 71971181 and 72032005 ), Research Grant Council of Hong Kong ( 11203519 and 11200621 ). It was also funded by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).

Research Keywords

  • Adaptive control
  • Model uncertainty
  • Robust control
  • Wheeled mobile robot

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