eprintid: 17196
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/71/96
datestamp: 2023-12-19 03:23:38
lastmod: 2023-12-19 03:23:38
status_changed: 2023-12-19 03:07:38
type: article
metadata_visibility: show
creators_name: Jian, N.L.
creators_name: Zabiri, H.
creators_name: Ramasamy, M.
title: Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control
ispublished: pub
keywords: Controllers; Errors; Mean square error; Predictive control systems; Recurrent neural networks, Auto-regressive; Model predictive controllers; Model-predictive control; Multiple timescale recurrent neural networks; Network-based; Network-based modeling; Neural-networks; Predictive controller; Set-point tracking; Time-scales, Model predictive control
note: cited By 0
abstract: 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.
date: 2022
publisher: American Chemical Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153038760&doi=10.1021%2facs.iecr.2c04114&partnerID=40&md5=9ec40e6dde44a07a8dcfe5533703c3d7
id_number: 10.1021/acs.iecr.2c04114
full_text_status: none
publication: Industrial and Engineering Chemistry Research
refereed: TRUE
issn: 08885885
citation:   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.   ISSN 08885885