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Manipulator path control with both integer and non-integer order derivative operators

  • Silesian University of Technology

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

Abstract

In this paper an application of the fractional calculus to path control is studied. The integer-order derivative and integral are replaced with the fractional-order ones in order to solve the inverse kinematics problem. The proposed algorithm is a modification of the existing one. In order to maintain the accuracy and to lower the memory requirements a history limit and a combination of fractional and integer-order derivation are proposed. After reaching assumed accuracy or iteration limit the algorithm switches to integer order derivative and stops after few additional iterations. This approach allows to reduce the positional error and maintain the repeatability of fractional calculus approach. The simulated path in task space have been designed in a way that causes the instability of standard Closed Loop Pseudoinverse algorithm. Our study proves that use of fractional calculus may improve the joint paths.

Original languageEnglish
Title of host publicationProceedings of 2016 Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-101
Number of pages5
ISBN (Electronic)9781509013623
DOIs
Publication statusPublished - 29 Aug 2016
Event2016 Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2016 - Tokyo, Japan
Duration: 20 Jul 201622 Jul 2016

Publication series

NameProceedings of 2016 Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2016

Conference

Conference2016 Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2016
Country/TerritoryJapan
CityTokyo
Period20/07/1622/07/16

Keywords

  • approximation
  • fractional calculus
  • inverse kinematics
  • manipulator
  • path planning
  • t-integrator

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Control and Optimization

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