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Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme

Problem statement: This paper presents an overview of a controller for a Rotational Inverted Pendulum (RIP) based on a New Recurrent Neural Network (NRNN) using Internal Model control (IMC). The RIP consists of a DC servo motor, arm and pendulum. The RIP is modelled in MATLAB/Simulink and the simula...

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Main Authors: Shojaei, A. A., Othman, Mohd. Fauzi, Rahmani, R., Rani, M. R.
Format: Article
Published: Elsevier 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/33922/
http://www.ajbasweb.com/old/ajbas/2012/July/299-306.pdf
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spelling my.utm.339222019-03-31T08:31:08Z http://eprints.utm.my/id/eprint/33922/ Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme Shojaei, A. A. Othman, Mohd. Fauzi Rahmani, R. Rani, M. R. TK Electrical engineering. Electronics Nuclear engineering Problem statement: This paper presents an overview of a controller for a Rotational Inverted Pendulum (RIP) based on a New Recurrent Neural Network (NRNN) using Internal Model control (IMC). The RIP consists of a DC servo motor, arm and pendulum. The RIP is modelled in MATLAB/Simulink and the simulation results are shown besides the experimental results. The proposed experiment shows intelligent method for stabilizing the RIP, which can recommend the control designers of nonlinear systems. The outcome exposed that the NRNN controller competent of controlling the RIP system productively, as exposed in the simulation results. Elsevier 2012-07 Article PeerReviewed Shojaei, A. A. and Othman, Mohd. Fauzi and Rahmani, R. and Rani, M. R. (2012) Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme. Australian Journal of Basic and Applied Sciences, 6 (7). pp. 299-306. ISSN 1991-8178 http://www.ajbasweb.com/old/ajbas/2012/July/299-306.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Shojaei, A. A.
Othman, Mohd. Fauzi
Rahmani, R.
Rani, M. R.
Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme
description Problem statement: This paper presents an overview of a controller for a Rotational Inverted Pendulum (RIP) based on a New Recurrent Neural Network (NRNN) using Internal Model control (IMC). The RIP consists of a DC servo motor, arm and pendulum. The RIP is modelled in MATLAB/Simulink and the simulation results are shown besides the experimental results. The proposed experiment shows intelligent method for stabilizing the RIP, which can recommend the control designers of nonlinear systems. The outcome exposed that the NRNN controller competent of controlling the RIP system productively, as exposed in the simulation results.
format Article
author Shojaei, A. A.
Othman, Mohd. Fauzi
Rahmani, R.
Rani, M. R.
author_facet Shojaei, A. A.
Othman, Mohd. Fauzi
Rahmani, R.
Rani, M. R.
author_sort Shojaei, A. A.
title Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme
title_short Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme
title_full Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme
title_fullStr Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme
title_full_unstemmed Implementation of recurrent neural network to control rotational inverted pendulum using IMC scheme
title_sort implementation of recurrent neural network to control rotational inverted pendulum using imc scheme
publisher Elsevier
publishDate 2012
url http://eprints.utm.my/id/eprint/33922/
http://www.ajbasweb.com/old/ajbas/2012/July/299-306.pdf
_version_ 1643649470594809856
score 13.159267

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