Title: Adaptive ANN-based control for constrained robot manipulators
Author: F. Mnif, A. Gastli, M. Jallouli
Department of Electrical and Computer Engineering, Sultan Qaboos University P.O. Box 33, Muscat, Oman; Intelligent Control, Design and Optimization of Complex Systems Laboratory, Ecole Nationale d
Ingenieurs de Sfax, Tunisia.
Department of Electrical and Computer Engineering, Sultan Qaboos University P.O. Box 33, Muscat, Oman.
Intelligent Control, Design and Optimization of Complex Systems Laboratory, Ecole Nationale d
Ingenieurs de Sfax, Tunisia
Abstract: An Artificial Neural Network (ANN)-based approach is proposed in this paper for the motion and force control of constrained robot manipulators. The dynamic model of constrained manipulator is modified to contain two sets of state variables, where one describes the constrained motion and the other describes the reduced, unconstrained one. An application related to a disk cutter robot is considered. The design objective is that under a prescribed radius of the disk and a desired force to be applied by the effector of the manipulator, an ANN-based control system is to be designed to develop the requested torques on the manipulator actuators. The suggested controller is simple and can be implemented easily. The main feature of employing the ANN here is that we obtained a force sensor-less control system and a good adaptation with environmental unmodelled dynamics. Simulation results are presented to validate the proposed approach.
Keywords: constrained manipulators; neural network estimation; adaptive control; robot control; neural networks; intelligent systems; motion control; force control; dynamic modelling; disk cutting; simulation; robotics.
Int. J. of Intelligent Systems Technologies and Applications, 2007 Vol.2, No.1, pp.77 - 99
Available online: 02 Dec 2006