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A metamorphic controller for plant control system design

Authors :
Tomasz Klopot
Piotr Skupin
Dariusz Choinski
Rafal Cupek
Marcin Fojcik
Source :
Modeling, Identification and Control, Vol 37, Iss 3, Pp 159-169 (2016)
Publication Year :
2016
Publisher :
Norwegian Society of Automatic Control, 2016.

Abstract

One of the major problems in the design of industrial control systems is the selection and parameterization of the control algorithm. In practice, the most common solution is the PI (proportional-integral) controller, which is simple to implement, but is not always the best control strategy. The use of more advanced controllers may result in a better efficiency of the control system. However, the implementation of advanced control algorithms is more time-consuming and requires specialized knowledge from control engineers. To overcome these problems and to support control engineers at the controller design stage, the paper describes a tool, i.e., a metamorphic controller with extended functionality, for selection and implementation of the most suitable control algorithm. In comparison to existing solutions, the main advantage of the metamorphic controller is its possibility of changing the control algorithm. In turn, the candidate algorithms can be tested through simulations and the total time needed to perform all simulations can be less than a few minutes, which is less than or comparable to the design time in the concurrent design approach. Moreover, the use of well-known tuning procedures, makes the system easy to understand and operate even by inexperienced control engineers. The application was implemented in the real industrial programmable logic controller (PLC) and tested with linear and nonlinear virtual plants. The obtained simulation results confirm that the change of the control algorithm allows the control objectives to be achieved at lower costs and in less time.

Details

Language :
English
ISSN :
03327353 and 18901328
Volume :
37
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Modeling, Identification and Control
Publication Type :
Academic Journal
Accession number :
edsdoj.0ac99d0c34c348449b544645dfdd7
Document Type :
article
Full Text :
https://doi.org/10.4173/mic.2016.3.2