Publication:
Trajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process

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dc.contributor.authorERDAL, HASAN
dc.contributor.authorsUnal, Muhammet; Erdal, Hasan; Topuz, Vedat
dc.date.accessioned2022-03-12T18:05:16Z
dc.date.available2022-03-12T18:05:16Z
dc.date.issued2012
dc.description.abstractThe main goal of this study was to compare the performances of genetic algorithm (GA) and ant colony optimization (ACO) algorithm for PID controller tuning on a pressure control process. GA and ACO were used for tuning of the PID controller when predefined trajectory reference signal was applied. Offline learning approach was employed in both GA and ACO algorithms. Realized pressure process dynamic has nonlinear behavior, thus system was modeled by nonlinear auto regressive and exogenous input (NARX) type artificial neural network (ANN) approach. PID controller was also tuned by ZieglerNichols (ZN) method to compare the results. A cost function was design to minimize the error along the defined cubic trajectory for the GA-PID and ACO-PID controller. Then PID controller parameters (Kp, Ki, Kd) were found by GA-PID, ACO-PID algorithms, which were adjusted with their optimal parameters. It was concluded that both ACO and GA algorithms could be used to tune the PID controllers in the pressure process with excellent performance. This material is suitable for an engineering course on neural networks, genetic algorithm, ant colony optimization and process control laboratory. (c) 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 518528, 2012
dc.identifier.doi10.1002/cae.20420
dc.identifier.eissn1099-0542
dc.identifier.issn1061-3773
dc.identifier.urihttps://hdl.handle.net/11424/230650
dc.identifier.wosWOS:000307221200015
dc.language.isoeng
dc.publisherWILEY
dc.relation.ispartofCOMPUTER APPLICATIONS IN ENGINEERING EDUCATION
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectpressure process
dc.subjectant colony optimization algorithm
dc.subjectgenetic algorithm
dc.subjectPID controller
dc.subjectartificial neural network
dc.titleTrajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process
dc.typearticle
dspace.entity.typePublication
local.avesis.id3a5a9aaf-5bc4-4cbd-a030-b439383d2a4a
local.import.packageSS17
local.indexed.atWOS
local.indexed.atSCOPUS
local.journal.numberofpages11
oaire.citation.endPage528
oaire.citation.issue3
oaire.citation.startPage518
oaire.citation.titleCOMPUTER APPLICATIONS IN ENGINEERING EDUCATION
oaire.citation.volume20
relation.isAuthorOfPublication819a2e66-ead9-43dc-b167-2990f1787c41
relation.isAuthorOfPublication.latestForDiscovery819a2e66-ead9-43dc-b167-2990f1787c41

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