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Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control

This study attempts to offer an alternative to the problem of implementing model predictive controllers (MPC) in conditions where the timescale multiplicity of the process model is not accounted for when incorporated into the MPC. Modeling methods that do not account for the timescale multiplicity i...

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Main Authors: Jian, N.L., Zabiri, H., Ramasamy, M.
Format: Article
Published: 2022
Online Access:http://scholars.utp.edu.my/id/eprint/37625/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153038760&doi=10.1021%2facs.iecr.2c04114&partnerID=40&md5=9ec40e6dde44a07a8dcfe5533703c3d7
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spelling oai:scholars.utp.edu.my:376252023-10-17T02:16:31Z http://scholars.utp.edu.my/id/eprint/37625/ Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control Jian, N.L. Zabiri, H. Ramasamy, M. This study attempts to offer an alternative to the problem of implementing model predictive controllers (MPC) in conditions where the timescale multiplicity of the process model is not accounted for when incorporated into the MPC. Modeling methods that do not account for the timescale multiplicity in system�s dynamics tend to become ill-conditioned and stiff when inversed in model-based controllers, thus requiring high computational loads to solve the equations. Therefore, this study proposes an alternative approach to the control of multi-timescale processes based on the use of multiple timescale recurrent neural network (MTRNN)-based neural network predictive controllers (NNPC). The effectiveness in handling setpoint tracking scenarios by the proposed method is evaluated using a benchmark nonexplicit two-timescale continuous stirred tank reactor (CSTR). After undergoing controller parameter optimization, the optimum configuration is found to be at 110, 37, and 0.2 for the cost horizon, control horizon, and control weighting factor, respectively. Results show that the MTRNN-based NNPC is able to track the reference trajectory with stable response and minimal error with a root mean square error of 0.0642. The optimized MTRNN-based controller is tested for its robustness under plant-model mismatch and is compared for its setpoint tracking abilities with a nonlinear autoregressive exogeneous (NARX)-based NNPC which showed that the proposed controller can satisfy the desired setpoint, resulting in an error that is 1.8 times lower than NARX-based NNPC. © 2023 American Chemical Society. 2022 Article NonPeerReviewed Jian, N.L. and Zabiri, H. and Ramasamy, M. (2022) Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control. Industrial and Engineering Chemistry Research. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153038760&doi=10.1021%2facs.iecr.2c04114&partnerID=40&md5=9ec40e6dde44a07a8dcfe5533703c3d7 10.1021/acs.iecr.2c04114 10.1021/acs.iecr.2c04114 10.1021/acs.iecr.2c04114
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 study attempts to offer an alternative to the problem of implementing model predictive controllers (MPC) in conditions where the timescale multiplicity of the process model is not accounted for when incorporated into the MPC. Modeling methods that do not account for the timescale multiplicity in system�s dynamics tend to become ill-conditioned and stiff when inversed in model-based controllers, thus requiring high computational loads to solve the equations. Therefore, this study proposes an alternative approach to the control of multi-timescale processes based on the use of multiple timescale recurrent neural network (MTRNN)-based neural network predictive controllers (NNPC). The effectiveness in handling setpoint tracking scenarios by the proposed method is evaluated using a benchmark nonexplicit two-timescale continuous stirred tank reactor (CSTR). After undergoing controller parameter optimization, the optimum configuration is found to be at 110, 37, and 0.2 for the cost horizon, control horizon, and control weighting factor, respectively. Results show that the MTRNN-based NNPC is able to track the reference trajectory with stable response and minimal error with a root mean square error of 0.0642. The optimized MTRNN-based controller is tested for its robustness under plant-model mismatch and is compared for its setpoint tracking abilities with a nonlinear autoregressive exogeneous (NARX)-based NNPC which showed that the proposed controller can satisfy the desired setpoint, resulting in an error that is 1.8 times lower than NARX-based NNPC. © 2023 American Chemical Society.
format Article
author Jian, N.L.
Zabiri, H.
Ramasamy, M.
spellingShingle Jian, N.L.
Zabiri, H.
Ramasamy, M.
Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
author_facet Jian, N.L.
Zabiri, H.
Ramasamy, M.
author_sort Jian, N.L.
title Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
title_short Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
title_full Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
title_fullStr Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
title_full_unstemmed Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
title_sort control of the multi-timescale process using multiple timescale recurrent neural network-based model predictive control
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/37625/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153038760&doi=10.1021%2facs.iecr.2c04114&partnerID=40&md5=9ec40e6dde44a07a8dcfe5533703c3d7
_version_ 1781707934239031296
score 13.211869

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