Neuro-fuzzy control of rigid and flexible-joint robotic manipulator

Author

Pletl, Szilveszter

Author_Institution

Inst. of Electro-Mech. Syst., Subotica, Yugoslavia

Volume

1

fYear

1995

fDate

6-10 Nov 1995

Firstpage

93

Abstract

Robot control can be decomposed into specification, control algorithm, sensory and torque control levels. The paper deals with the control algorithm level of industrial robots. In this paper, a neural implementation of the fuzzy controller has been proposed. A neuro-fuzzy controller (NFC) is used for the trajectory tracking of four degree-of-freedom rigid-link flexible and rigid joint SCARA-type manipulators. Online fine tuning of NFCs is proposed using the backpropagation algorithm. This paper contains a comparison of NFCs before and after fine tuning. A traditional fuzzy controller is compared with an NFC, using a simulation method, with respect to the trajectory tracking control of a SCARA-type manipulator. The results illustrate the usefulness of NFCs

Keywords

backpropagation; control system analysis; control system synthesis; fuzzy control; fuzzy neural nets; industrial manipulators; neurocontrollers; position control; tracking; SCARA-type manipulators; backpropagation algorithm; control algorithm; control design; control simulation method; degree-of-freedom; flexible joints; industrial robot manipulators; neuro-fuzzy control; online fine tuning; rigid joints; trajectory tracking control; Biological neural networks; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Manipulators; Neurons; Robots; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-3026-9

Type

conf

DOI

10.1109/IECON.1995.483339

Filename

483339