Fuzzy Variable Structure Control of a Class of Nonlinear Sampled-Data Systems

Authors:
Mehmet Önder Efe
Affiliations:
Electrical Engineering Department, The Ohio State University, Columbus, OH 43210, U.S.A. E-mail: onderefe@ieee.org
Venue:
Journal of Dynamical and Control Systems
Year:
2003

Citing 5
Cited 0

Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
Intelligent control for autonomous systems

IEEE Spectrum
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A novel optimization procedure for training of fuzzy inference systems by combining variable structure systems technique and Levenberg-Marquardt algorithm

Fuzzy Sets and Systems
Fuzzy Control

Fuzzy Control

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel approach to the fuzzy variable structure control is presented. The method is applicable to a class of discrete-time multi-input, multi-output systems. The controller for each subsystem is a two-input single-output fuzzy inference system partitioning the input space. The scheme presented analytically demonstrates that an appropriate tuning of the defuzzifier parameters can drive the plant to the desired sliding regime. The analysis begins with the extraction of the equivalent measure of the applied control signal, and continues with the proof of convergence claims for the discrete time sliding mode control. The method discussed has been applied to a double pendulum system, whose dynamics is assumed to be unknown, and the mathematical claims of the paper have been justified through a series of simulations. The results observed strongly recommend the use of the algorithm in the cases where the tracking precision and robustness against disturbances are sought.