Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers

Fusaomi Nagata, Katsutoshi Kuribayashi, Kazuo Kiguchi, Keigo Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Resolved acceleration control method or computed torque method is used for nonlinear control of industrial manipulators, which is composed of a model base portion and a servo portion. The servo portion is a close loop with respect to the position and velocity. On the other hand, the model base portion has the inertia term, gravity term and centrifugal/Coriolis term, which work for canceling the nonlinearity of manipulator. In order to realize high control stability, the position and velocity gains used in the servo portion should be selected suitably. In this paper, a simple but effective fine tuning method after manual tuning is introduced for the position and velocity feedback gains in the servo portion. At the first step, base values of the gains are roughly selected by a controller designer, e.g., considering the critically damped condition. After that, the base values are finely tuned by genetic algorithms. Genetic algorithms search for the better combination of the position and velocity gains. Simulations are conducted using a dynamic model of PUMA560 manipulator to validate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
Pages196-201
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007 - Jacksonville, FL, United States
Duration: Jun 20 2007Jun 23 2007

Other

Other2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
CountryUnited States
CityJacksonville, FL
Period6/20/076/23/07

Fingerprint

Robotics
Tuning
Genetic algorithms
Controllers
Manipulators
Industrial manipulators
Acceleration control
Dynamic models
Gravitation
Torque
Feedback

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Nagata, F., Kuribayashi, K., Kiguchi, K., & Watanabe, K. (2007). Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers. In Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007 (pp. 196-201). [4269914] https://doi.org/10.1109/CIRA.2007.382914

Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers. / Nagata, Fusaomi; Kuribayashi, Katsutoshi; Kiguchi, Kazuo; Watanabe, Keigo.

Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007. 2007. p. 196-201 4269914.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nagata, F, Kuribayashi, K, Kiguchi, K & Watanabe, K 2007, Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers. in Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007., 4269914, pp. 196-201, 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007, Jacksonville, FL, United States, 6/20/07. https://doi.org/10.1109/CIRA.2007.382914
Nagata F, Kuribayashi K, Kiguchi K, Watanabe K. Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers. In Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007. 2007. p. 196-201. 4269914 https://doi.org/10.1109/CIRA.2007.382914
Nagata, Fusaomi ; Kuribayashi, Katsutoshi ; Kiguchi, Kazuo ; Watanabe, Keigo. / Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers. Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007. 2007. pp. 196-201
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