Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

Dechao Chen, Yunong Zhang, Shuai Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

79 Citations (Scopus)

Abstract

Tracking control of robot manipulators is a fundamental and significant problem in robotic industry. As a conventional solution, the Jacobian-matrix-pseudo-inverse (JMPI) method suffers from two major limitations: one is the requirement on known information of the robot model such as parameter and structure; the other is the position error accumulation phenomenon caused by the open-loop nature. To overcome such two limitations, this paper proposes a novel Jacobian-matrix-Adaption (JMA) method for the tracking control of robot manipulators via the zeroing dynamics. Unlike existing works requiring the information of the known robot model, the proposed JMA method uses only the input-output information to control the robot with unknown model. The solution based on the JMA method transforms the internal, implicit, and unmeasurable model information to the external, explicit, and measurable input-output information. Moreover, simulation studies including comparisons and tests substantiate the efficacy and superiority of the proposed JMA method for the tracking control of robot manipulators subject to unknown models.

Original languageEnglish
Pages (from-to)3044-3053
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number7
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • Input-output information
  • Jacobian-matrix-Adaption (JMA)
  • robot manipulators
  • unknown models
  • zeroing dynamics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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