Hybrid position/force control of the SCORBOT-ER 4pc manipulator with neural compensation of nonlinearities

Authors:
Piotr Gierlak
Affiliations:
Department of Applied Mechanics and Robotics, Rzeszow University of Technology, Rzeszów, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Year:
2012

Citing 4
Cited 0

Neural network for robotic control

Neural network for robotic control
Theory of Robot Control

Theory of Robot Control
Neural Network Control of Robot Manipulators and Nonlinear Systems

Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural net robot controller with guaranteed tracking performance

IEEE Transactions on Neural Networks

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Abstract

The problem of the manipulator hybrid position/force control is not trivial because the manipulator is a nonlinear object, whose parameters may be unknown, variable and the working conditions are changeable. The neural control system enables the manipulator to behave correctly, even if the mathematical model of the control object is unknown. In this paper, the hybrid position/force controller with a neural compensation of nonlinearities for the SCORBOT-ER 4pc robotic manipulator is presented. The presented control law and adaptive law guarantee practical stability of the closed-loop system in the sense of Lyapunov. The results of a numerical simulation are presented.