Tracking Control of Fully-Constrained Cable-Driven Parallel Robots using Adaptive Dynamic Programming

Shuai Li, Damiano Zanotto

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

4 Scopus citations

Abstract

In this paper, a new adaptive tracking controller with learning ability is proposed for fully-constrained cable-driven parallel robots (CDPRs). For these systems, the necessity of maintaining positive and bounded tensions in all cables while coping with disturbances represents a critical control requirement. To achieve this goal, we propose a control law based on adaptive dynamic programming (ADP), with an actorcritic structure. In the critic part, an artificial neural network (NN) approximates the value function which is to evaluate the system performance; in the action part, the controller's parameters are tuned online to achieve optimal control performance. Additionally, the anti-windup (AW) technique is combined with the adaptive controller to cope with the input saturation problem. The stability of the closed-loop system with the proposed control algorithm is proved using the Lyapunov method. Numerical simulations show the effectiveness of the proposed controller.

Original languageEnglish
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Pages6781-6787
Number of pages7
ISBN (Electronic)9781728140049
DOIs
StatePublished - Nov 2019
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: 3 Nov 20198 Nov 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Country/TerritoryChina
CityMacau
Period3/11/198/11/19

Keywords

  • adaptive dynamic programming
  • anti-windup
  • cable-driven parallel robots
  • neural networks
  • tracking control.

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