Inverse dynamics based robot control method using fuzzy identifiers

Kemalettin Erbatur, Okyay Kaynak, I. Rudas

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

3 Citations (Scopus)

Abstract

The trajectory control of robotic manipulators requires a model that can overcome the problems of coupled and nonlinear dynamics. Fuzzy logic systems which are represented as three-layer feedforward neural networks can be used to match the gravity, centripetal, Coriolis and inertial effects in the robot dynamic model. The dynamic parameters are learned while the robot arm is partly controlled by a simple proportional differential (PD) control algorithm.

Original languageEnglish
Title of host publicationIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
PublisherIEEE
Pages102
Number of pages1
Publication statusPublished - 1997
EventProceedings of the 1997 1st IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM'97 - Tokyo, Jpn
Duration: Jun 16 1997Jun 20 1997

Other

OtherProceedings of the 1997 1st IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM'97
CityTokyo, Jpn
Period6/16/976/20/97

Fingerprint

Robots
Feedforward neural networks
Fuzzy logic
Manipulators
Dynamic models
Gravitation
Robotics
Trajectories

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Erbatur, K., Kaynak, O., & Rudas, I. (1997). Inverse dynamics based robot control method using fuzzy identifiers. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM (pp. 102). IEEE.

Inverse dynamics based robot control method using fuzzy identifiers. / Erbatur, Kemalettin; Kaynak, Okyay; Rudas, I.

IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. IEEE, 1997. p. 102.

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

Erbatur, K, Kaynak, O & Rudas, I 1997, Inverse dynamics based robot control method using fuzzy identifiers. in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. IEEE, pp. 102, Proceedings of the 1997 1st IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM'97, Tokyo, Jpn, 6/16/97.
Erbatur K, Kaynak O, Rudas I. Inverse dynamics based robot control method using fuzzy identifiers. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. IEEE. 1997. p. 102
Erbatur, Kemalettin ; Kaynak, Okyay ; Rudas, I. / Inverse dynamics based robot control method using fuzzy identifiers. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. IEEE, 1997. pp. 102
@inproceedings{416effdfca8146aaac49bd2d380a33a9,
title = "Inverse dynamics based robot control method using fuzzy identifiers",
abstract = "The trajectory control of robotic manipulators requires a model that can overcome the problems of coupled and nonlinear dynamics. Fuzzy logic systems which are represented as three-layer feedforward neural networks can be used to match the gravity, centripetal, Coriolis and inertial effects in the robot dynamic model. The dynamic parameters are learned while the robot arm is partly controlled by a simple proportional differential (PD) control algorithm.",
author = "Kemalettin Erbatur and Okyay Kaynak and I. Rudas",
year = "1997",
language = "English",
pages = "102",
booktitle = "IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM",
publisher = "IEEE",

}

TY - GEN

T1 - Inverse dynamics based robot control method using fuzzy identifiers

AU - Erbatur, Kemalettin

AU - Kaynak, Okyay

AU - Rudas, I.

PY - 1997

Y1 - 1997

N2 - The trajectory control of robotic manipulators requires a model that can overcome the problems of coupled and nonlinear dynamics. Fuzzy logic systems which are represented as three-layer feedforward neural networks can be used to match the gravity, centripetal, Coriolis and inertial effects in the robot dynamic model. The dynamic parameters are learned while the robot arm is partly controlled by a simple proportional differential (PD) control algorithm.

AB - The trajectory control of robotic manipulators requires a model that can overcome the problems of coupled and nonlinear dynamics. Fuzzy logic systems which are represented as three-layer feedforward neural networks can be used to match the gravity, centripetal, Coriolis and inertial effects in the robot dynamic model. The dynamic parameters are learned while the robot arm is partly controlled by a simple proportional differential (PD) control algorithm.

UR - http://www.scopus.com/inward/record.url?scp=0031370010&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031370010&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0031370010

SP - 102

BT - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

PB - IEEE

ER -