Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems

Grelewicz, P; Khuat, TT; Czeczot, J; Nowak, P; Klopot, T; Gabrys, B

Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems

Grelewicz, P Khuat, TT Czeczot, J Nowak, P Klopot, T Gabrys, B

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53, (7), pp. 4226-4238
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Grelewicz, P
dc.contributor.author	Khuat, TT
dc.contributor.author	Czeczot, J
dc.contributor.author	Nowak, P
dc.contributor.author	Klopot, T
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date.accessioned	2024-01-11T03:30:08Z
dc.date.available	2024-01-11T03:30:08Z
dc.date.issued	2023-07-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53, (7), pp. 4226-4238
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/174291
dc.description.abstract	In this article, a novel machine learning (ML)-derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed-loop performance. This approach allows for determining the best (but also robust and practically achievable) closed-loop performance based on very popular and intuitive closed-loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different ML-based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed-loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2023.3244714
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems
dc.type	Journal Article
utslib.citation.volume	53
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-02-28T00:00:00+1000Z
dc.date.updated	2024-01-11T03:29:56Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	7

Abstract:

In this article, a novel machine learning (ML)-derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed-loop performance. This approach allows for determining the best (but also robust and practically achievable) closed-loop performance based on very popular and intuitive closed-loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different ML-based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed-loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.

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http://hdl.handle.net/10453/174291