Statistical measures for proportional–integral–derivative control quality: Simulations and industrial data
AbstractThis 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.
|Journal series||Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, ISSN 0959-6518, (A 20 pkt)|
|Publication size in sheets||4798259088770.15|
|Keywords in English||Controller performance assessment, proportional–integral–derivative control, non-Gaussian distributions, a-stable probabilistic distribution function, industrial data|
|Project||Development 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
|Score||= 20.0, 19-12-2019, ArticleFromJournal|
|Publication indicators||= 4; = 0; : 2016 = 0.743; : 2018 = 1.166 (2) - 2018=1.204 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.