Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems

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

Permalink

Publication Type:: Journal Article
Citation:: 2021
Issue Date:: 2021-01-08

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (2.88 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Grelewicz, P
dc.contributor.author	Khuat, TT
dc.contributor.author	Czeczot, J
dc.contributor.author	Klopot, T
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date.accessioned	2021-05-04T02:47:28Z
dc.date.available	2021-05-04T02:47:28Z
dc.date.issued	2021-01-08
dc.identifier.citation	2021
dc.identifier.uri	http://hdl.handle.net/10453/148600
dc.description.abstract	In this paper, a novel machine learning derived control performance assesment (CPA) classification system is proposed. It is dedicated for a class of PID-based control loops with processes exhibiting second order plus delay time (SOPDT) dynamical properties. The proposed concept is based on deriving and combining a number of different, diverse control performance indices (CPIs) that separately do not provide sufficient information about the control performance. However, when combined together and used as discriminative features of the assessed control system, they can provide consistent and accurate CPA information. This concept is discussed in terms of the introduced extended set of CPIs, comprehensive performance assessment of different machine learning based classification methods and practical applicability of the suggested solution. The latter is shown and verified by practical application of the proposed approach to a CPA system for a laboratory heat exchange and ditribution setup.
dc.language	en
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems
dc.type	Journal Article
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/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-04T02:47:17Z

Abstract:

In this paper, a novel machine learning derived control performance assesment (CPA) classification system is proposed. It is dedicated for a class of PID-based control loops with processes exhibiting second order plus delay time (SOPDT) dynamical properties. The proposed concept is based on deriving and combining a number of different, diverse control performance indices (CPIs) that separately do not provide sufficient information about the control performance. However, when combined together and used as discriminative features of the assessed control system, they can provide consistent and accurate CPA information. This concept is discussed in terms of the introduced extended set of CPIs, comprehensive performance assessment of different machine learning based classification methods and practical applicability of the suggested solution. The latter is shown and verified by practical application of the proposed approach to a CPA system for a laboratory heat exchange and ditribution setup.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148600