A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The su...

Full description

Saved in:
Bibliographic Details
Published in:Mechatronics (Oxford) 2001-03, Vol.11 (2), p.227-250
Main Authors: Lin, F.J., Fung, R.F., Lin, H.H., Hong, C.M.
Format: Article
Language:English
Subjects:
Applied sciences
Computers in experimental physics
Drives
Exact sciences and technology
Instruments, apparatus, components and techniques common to several branches of physics and astronomy
Mechanical engineering. Machine design
Neural networks, fuzzy logic, artificial intelligence
Physics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.
ISSN:0957-4158
1873-4006
DOI:10.1016/S0957-4158(99)00070-7