Statistical measures for proportional–integral–derivative control quality: Simulations and industrial data

Paweł Domański


This article focuses on investigation of statistical approaches to the task of control performance assessment. Different statistical measures with Gaussian and non-Gaussian probabilistic distributions are taken into consideration. Analysis starts with the observations for simulated proportional–integral–derivative control error histograms followed by its statistical investigation using selected probabilistic distribution functions. Simulation experiments are followed by the analysis of control data originating from real industrial loops. Shadowing effect of long-tail control error histograms is identified, as it may significantly disable proper loop quality assessment. Results show that non-Gaussian approach with Cauchy or a-stable distributions seems to be reasonable assessment alternative in case of disturbances existing in industrial processes.
Author Paweł Domański (FEIT / AK)
Paweł Domański,,
- The Institute of Control and Computation Engineering
Journal seriesProceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, ISSN 0959-6518, (A 20 pkt)
Issue year2018
Publication size in sheets4798259088770.15
Keywords in EnglishController performance assessment, proportional–integral–derivative control, non-Gaussian distributions, a-stable probabilistic distribution function, industrial data
ASJC Classification2210 Mechanical Engineering; 2207 Control and Systems Engineering
ProjectDevelopment of methodology of control, decision support and production management. Project leader: Ogryczak Włodzimierz, , Phone: 6190, start date 04-05-2016, end date 31-12-2017, 504/statut2016/1031, Completed
WEiTI Działalność statutowa
Languageen angielski
Domanski P 2018 IJSCE.pdf 4.82 MB
Score (nominal)20
Score sourcejournalList
ScoreMinisterial score = 20.0, 19-12-2019, ArticleFromJournal
Publication indicators Scopus Citations = 4; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.743; WoS Impact Factor: 2018 = 1.166 (2) - 2018=1.204 (5)
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