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Machine learning application to industrial control systems

This chapter focuses on the growth, development, and future of various machine learning techniques in industrial process control applications. However, the main focus is developing a neural network (NN)-based predictive controller to control the neutralization process using a continuous stirred tank...

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Main Authors: Bingi, K., Prusty, B.R., Ibrahim, R.
Format: Book
Published: Elsevier 2022
Online Access:http://scholars.utp.edu.my/id/eprint/34108/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137553296&doi=10.1016%2fB978-0-323-90789-7.00008-7&partnerID=40&md5=ac240fd2a5fc351e8e560de4dc2c7f1e
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spelling oai:scholars.utp.edu.my:341082023-01-03T07:23:00Z http://scholars.utp.edu.my/id/eprint/34108/ Machine learning application to industrial control systems Bingi, K. Prusty, B.R. Ibrahim, R. This chapter focuses on the growth, development, and future of various machine learning techniques in industrial process control applications. However, the main focus is developing a neural network (NN)-based predictive controller to control the neutralization process using a continuous stirred tank reactor (CSTR). In the first stage of the proposed strategy, a nonlinear autoregressive moving average model will be developed to model the CSTR's nonlinearities, sensitive, and dynamic behavior. The training of the NN model is based on Levenberg-Marquardt algorithm. Then, for the developed model, a feedback linearization-based controller will be designed. The performance evaluation of the proposed modeling will be done on R2 and mean square errors. Moreover, the performance of the proposed control strategy will be evaluated and compared with the benchmark control law for set-point tracking. Furthermore, the numerical evaluation will be done using step-response characteristics such as rise, settling, and overshoot. © 2022 Elsevier Inc. All rights reserved. Elsevier 2022 Book NonPeerReviewed Bingi, K. and Prusty, B.R. and Ibrahim, R. (2022) Machine learning application to industrial control systems. Elsevier, pp. 237-258. ISBN 9780323907897; 9780323914413 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137553296&doi=10.1016%2fB978-0-323-90789-7.00008-7&partnerID=40&md5=ac240fd2a5fc351e8e560de4dc2c7f1e 10.1016/B978-0-323-90789-7.00008-7 10.1016/B978-0-323-90789-7.00008-7 10.1016/B978-0-323-90789-7.00008-7
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This chapter focuses on the growth, development, and future of various machine learning techniques in industrial process control applications. However, the main focus is developing a neural network (NN)-based predictive controller to control the neutralization process using a continuous stirred tank reactor (CSTR). In the first stage of the proposed strategy, a nonlinear autoregressive moving average model will be developed to model the CSTR's nonlinearities, sensitive, and dynamic behavior. The training of the NN model is based on Levenberg-Marquardt algorithm. Then, for the developed model, a feedback linearization-based controller will be designed. The performance evaluation of the proposed modeling will be done on R2 and mean square errors. Moreover, the performance of the proposed control strategy will be evaluated and compared with the benchmark control law for set-point tracking. Furthermore, the numerical evaluation will be done using step-response characteristics such as rise, settling, and overshoot. © 2022 Elsevier Inc. All rights reserved.
format Book
author Bingi, K.
Prusty, B.R.
Ibrahim, R.
spellingShingle Bingi, K.
Prusty, B.R.
Ibrahim, R.
Machine learning application to industrial control systems
author_facet Bingi, K.
Prusty, B.R.
Ibrahim, R.
author_sort Bingi, K.
title Machine learning application to industrial control systems
title_short Machine learning application to industrial control systems
title_full Machine learning application to industrial control systems
title_fullStr Machine learning application to industrial control systems
title_full_unstemmed Machine learning application to industrial control systems
title_sort machine learning application to industrial control systems
publisher Elsevier
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/34108/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137553296&doi=10.1016%2fB978-0-323-90789-7.00008-7&partnerID=40&md5=ac240fd2a5fc351e8e560de4dc2c7f1e
_version_ 1754532128121421824
score 13.222552

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