Robust Iterative Learning Control for Pneumatic Muscle with State Constraint and Model Uncertainty

Kun Qian, Zhenghong Li, Ahmed Asker, Zhiqiang Zhang, Shengquan Xie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a novel iterative learning control (ILC) scheme for precise state tracking of pneumatic muscle (PM) actuators. Two critical issues are considered in our scheme: 1) state constraints on PM position and velocity; 2) uncertainties of the PM model. Based on the three-element form, a PM model is constructed that takes both parametric and nonparametric uncertainties into consideration. By introducing the composite energy function (CEF) approach incorporated with a barrier Lyapunov function (BLF), full state constraints of PM will not be violated and uncertainties are effectively compensated. Through rigorous analysis, we show that under proposed ILC scheme, uniform convergence of PM state tracking errors are guaranteed. Simulation results validate the performance of the proposed scheme.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherIEEE
Pages5980-5987
Number of pages8
ISBN (Electronic)9781728190778
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: 30 May 20215 Jun 2021

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2021-May
ISSN (Print)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Country/TerritoryChina
CityXi'an
Period30/05/215/06/21

Fingerprint

Dive into the research topics of 'Robust Iterative Learning Control for Pneumatic Muscle with State Constraint and Model Uncertainty'. Together they form a unique fingerprint.

Cite this