Implementation of indirect neuro-control for nonlinear dynamic systems

This paper represents identification and control designs using neural networks for a class of nonlinear dynamic systems. The proposed neuro-controller is a combination of a linear controller and a neural network trained by an indirect neuro-control scheme. The proposed neuro-controller is implemente...

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Bibliographic Details
Published in:Mechatronics (Oxford) 1999, Vol.9 (6), p.675-686
Main Authors: Jang, Jun Oh, Jeon, Gi Joon
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Bar codes
Bar system
Computer science
control theory
systems
Control system analysis
Control system synthesis
Control theory. Systems
Exact sciences and technology
Indirect neuro-control
Linear control systems
Modelling and identification
Neural network identification
Neural networks
Neuro-controller
Nonlinear control systems
Precision engineering
Robotics
Servomechanisms
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Summary:This paper represents identification and control designs using neural networks for a class of nonlinear dynamic systems. The proposed neuro-controller is a combination of a linear controller and a neural network trained by an indirect neuro-control scheme. The proposed neuro-controller is implemented and tested on an IBM PC-based bar system, and is applicable to many dc-motor-driven precision servo mechanisms. The algorithm and experimental results are described. The experimental results are shown to be superior to those of conventional control.
ISSN:0957-4158
1873-4006
DOI:10.1016/S0957-4158(99)00011-2