Adaptive multi-model sliding mode control of robotic manipulators using soft computing

Authors:
Nasser Sadati;Rasoul Ghadami
Affiliations:
Intelligent Systems Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran;Intelligent Systems Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Venue:
Neurocomputing
Year:
2008

Citing 6
Cited 1

A course in fuzzy systems and control

A course in fuzzy systems and control
Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching

Fuzzy Sets and Systems
Introduction to Robotics: Mechanics and Control

Introduction to Robotics: Mechanics and Control
Fuzzy Sliding Mode Control for a Robot Manipulator

CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Neural network sliding mode robot control

Robotica
Functional equivalence between radial basis function networks and fuzzy inference systems

IEEE Transactions on Neural Networks

Nonlinear controller design of a ship autopilot

International Journal of Applied Mathematics and Computer Science

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, an adaptive multi-model sliding mode controller for robotic manipulators is presented. By using the multiple models technique, the nominal part of the control signal is constructed according to the most appropriate model at different environments. Adaptive single-input-single-output (SISO) fuzzy systems or radial basis function (RBF) neural networks, regarding their functional equivalence property, are used to approximate the discontinuous part of control signal; control gain, in a classical sliding mode controller. The key feature of this scheme is that prior knowledge of the system uncertainties is not required to guarantee the stability. Also the chattering phenomenon in sliding mode control and the steady-state tracking error are eliminated. Moreover, a theoretical proof of the stability and convergence of the proposed scheme using the Lyapunov method is presented.