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RAJCHL, M. BRABLC, M.
Original Title
Inverse Model Approximation Using Iterative Method and Neural Networks with Practical Application for Unstable Nonlinear System Control
Type
conference paper
Language
English
Original Abstract
In this paper a method for controlling and stabilizing an unstable nonlinear system using a NARX neural network is presented. It is difficult to design a common feedback controller or even perform system identification on unstable systems, more even so if these systems are nonlinear. To compensate for nonlinearity a feedforward controller is required. In this paper we present a method of estimating inverse model of the system for the feedforward controller using a NARX artificial neural network in an iterative approach which takes less time than methods commonly used and performs as good. This method is verified and tested on an educational model of magnetic levitation of steel ball. Both static and dynamic forms of the inverse model are presented and evaluated with positive results.
Keywords
nonlinear system, control, unstable, neural network, inverse model, magnetic levitation, PID, feedforward control
Authors
RAJCHL, M.; BRABLC, M.
Released
23. 1. 2019
ISBN
978-80-214-5542-9
Book
PROCEEDINGS OF THE 2018 18TH INTERNATIONAL CONFERENCE ON MECHATRONICS - MECHATRONIKA (ME)
Pages from
209
Pages to
215
Pages count
7
BibTex
@inproceedings{BUT152523, author="Matej {Rajchl} and Martin {Brablc}", title="Inverse Model Approximation Using Iterative Method and Neural Networks with Practical Application for Unstable Nonlinear System Control", booktitle="PROCEEDINGS OF THE 2018 18TH INTERNATIONAL CONFERENCE ON MECHATRONICS - MECHATRONIKA (ME)", year="2019", pages="209--215", isbn="978-80-214-5542-9" }