Genetic algorithm optimization analysis for temperature control system using cascade control loop model

This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with th...

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Main Authors: Chew, Ing, Wong, F., Bono, A., Nandong, Jobrun, Wong, Kiing
Format: Journal Article
Published: 2020
Online Access:http://hdl.handle.net/20.500.11937/85588
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author Chew, Ing
Wong, F.
Bono, A.
Nandong, Jobrun
Wong, Kiing
author_facet Chew, Ing
Wong, F.
Bono, A.
Nandong, Jobrun
Wong, Kiing
author_sort Chew, Ing
building Curtin Institutional Repository
collection Online Access
description This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with the IMC-based single loop tunings and conventional cascade control tunings. GA-based PID tunings eliminated the complicated mathematic calculations in obtaining the correlation PID tuning values and also reduce the dependency on engineering knowledge, experience, and skills. The performance of transient and steady-state responses was compared through time domain specification, performance index, and process response curve. It is concluded that the GA-based PID tunings for the cascade control loop had produced the best result for both servo and regulatory control objectives, which is eventually determined.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T11:24:14Z
publishDate 2020
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-855882021-09-30T01:03:44Z Genetic algorithm optimization analysis for temperature control system using cascade control loop model Chew, Ing Wong, F. Bono, A. Nandong, Jobrun Wong, Kiing This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with the IMC-based single loop tunings and conventional cascade control tunings. GA-based PID tunings eliminated the complicated mathematic calculations in obtaining the correlation PID tuning values and also reduce the dependency on engineering knowledge, experience, and skills. The performance of transient and steady-state responses was compared through time domain specification, performance index, and process response curve. It is concluded that the GA-based PID tunings for the cascade control loop had produced the best result for both servo and regulatory control objectives, which is eventually determined. 2020 Journal Article http://hdl.handle.net/20.500.11937/85588 10.12785/ijcds/090112 http://creativecommons.org/licenses/by-nc-nd/4.0/ fulltext
spellingShingle Chew, Ing
Wong, F.
Bono, A.
Nandong, Jobrun
Wong, Kiing
Genetic algorithm optimization analysis for temperature control system using cascade control loop model
title Genetic algorithm optimization analysis for temperature control system using cascade control loop model
title_full Genetic algorithm optimization analysis for temperature control system using cascade control loop model
title_fullStr Genetic algorithm optimization analysis for temperature control system using cascade control loop model
title_full_unstemmed Genetic algorithm optimization analysis for temperature control system using cascade control loop model
title_short Genetic algorithm optimization analysis for temperature control system using cascade control loop model
title_sort genetic algorithm optimization analysis for temperature control system using cascade control loop model
url http://hdl.handle.net/20.500.11937/85588