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Assessment of the GPC control quality using non-Gaussian statistical measures

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Języki publikacji
EN
Abstrakty
EN
This paper presents an alternative approach to the task of control performance assessment. Various statistical measures based on Gaussian and non-Gaussian distribution functions are evaluated. The analysis starts with the review of control error histograms followed by their statistical analysis using probability distribution functions. Simulation results obtained for a control system with the generalized predictive controller algorithm are considered. The proposed approach using Cauchy and Lévy α-stable distributions shows robustness against disturbances and enables effective control loop quality evaluation. Tests of the predictive algorithm prove its ability to detect the impact of the main controller parameters, such as the model gain, the dynamics or the prediction horizon.
Rocznik
Strony
291--307
Opis fizyczny
Bibliogr. 39 poz., tab., wykr.
Twórcy
  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
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  • [4] Borak, S., Misiorek, A. and Weron, R. (2011). Models for heavy-tailed asset returns, in P. Cizek et al. (Eds.), Statistical Tools for Finance and Insurance, 2nd Edn., Springer, New York, NY, pp. 21–56.
  • [5] Camacho, E.F. and Bordons, C. (1999). Model Predictive Control, Springer, London.
  • [6] Choudhury, M.A.A.S., Shah, S. and Thornhill, N. (2008). Diagnosis of Process Nonlinearities and Valve Stiction, Springer, Heidelberg.
  • [7] Clarke, W. and Mohtadi, C. (1989). Properties of generalized predictive control, Automatica 25(5): 859–875.
  • [8] Clarke, W., Mohtadi, C. and Tuffs, P.S. (1987a). Generalized predictive control. I: The basic algorithm, Automatica 23(2): 137–148.
  • [9] Clarke, W., Mohtadi, C. and Tuffs, P.S. (1987b). Generalized predictive control. II: Extensions and interpretations, Automatica 23(2): 149–160.
  • [10] Cramer, H. (1928). On the composition of elementary errors, Scandinavian Actuarial Journal 1928(1): 13–74.
  • [11] Cross, R. and Iqbal, A. (1995). The Rank Xerox experience: Benchmarking ten years on, in A. Rolstadås (Ed.), Benchmarking—Theory and Practice, Springer US, Boston, MA, pp. 3–10.
  • [12] Domański, P.D. (2015). Non-Gaussian properties of the real industrial control error in SISO loops, Proceedings of the 19th International Conference on System Theory, Control and Computing, Cheile Gradistei, Romania, pp. 877–882.
  • [13] Domański, P.D. (2016). Non-Gaussian and persistence measures for control loop quality assessment, Chaos: An Interdisciplinary Journal of Nonlinear Science 26(4): 043105.
  • [14] Eisenhart, C. (2006). Laws of error. III: Later (non-Gaussian) distributions, in S. Kotz et al. (Eds.), Encyclopedia of Statistical Sciences, Wiley, Hoboken, NJ, Chapter 6.
  • [15] Harris, T. (1989). Assessment of closed loop performance, Canadian Journal of Chemical Engineering 67(5): 856–861.
  • [16] Hill, I.D., Hill, R. and Holder, R.L. (1976). Algorithm AS 99: Fitting Johnson curves by moments, Journal of the Royal Statistical Society C: Applied Statistics 25(2): 180–189.
  • [17] Horch, A. and Isaksson, A.J. (1998). A modified index for control performance assessment, Proceedings of the 1998 American Control Conference, Philadelphia, PA, USA, Vol. 6, pp. 3430–3434.
  • [18] Hugo, A.J. (2006). Performance assessment of single-loop industrial controllers, Journal of Process Control 16(8): 785–794.
  • [19] Jelali, M. (2006). An overview of control performance assessment technology and industrial applications 14(5): 441–466.
  • [20] Jelali, M. (2013). Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance, Springer-Verlag, London.
  • [21] Koutrouvelis, I.A. (1980). Regression-type estimation of the parameters of stable laws, Journal of the American Statistical Association 75(372): 918–928.
  • [22] Ławryńczuk, M. (2015). Nonlinear state-space predictive control with on-line linearisation and state estimation, International Journal of Applied Mathematics and Computer Science 25(4): 833–847, DOI: 10.1515/amcs-2015-0060.
  • [23] Lilliefors, H. (1967). On the Kolmogorov–Smirnov test for normality with mean and variance unknown, Journal of the American Statistical Association 62(318): 399–402.
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  • [25] Ordys, A., Uduehi, D. and Johnson, M.A. (2007). Process Control Performance Assessment—From Theory to Implementation, Springer, London.
  • [26] Paulonis, M.A. and Cox, J.W. (2003). A practical approach for large-scale controller performance assessment, diagnosis, and improvement, Journal of Process Control 13(2): 155–168.
  • [27] Schäfer, J. and Cinar, A. (2004). Multivariable MPC system performance assessment, monitoring, and diagnosis, Journal of Process Control 14(2): 113–129.
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  • [30] Shinskey, F.G. (2002). Process control: As taught vs. as practiced, Industrial and Engineering Chemistry Research 41(16): 3745–3750.
  • [31] Smuts, J.F. and Hussey, A. (2011). Requirements for successfully implementing and sustaining advanced control applications, ISA POWID Conference, Research Triangle Park, NC, USA, pp. 89–105.
  • [32] Spinner, T., Srinivasan, B. and Rengaswamy, R. (2014). Data-based automated diagnosis and iterative retuning of proportional-integral (PI) controllers, Control Engineering Practice 29: 23–41.
  • [33] Srinivasan, B. and Rengaswamy, R. (2012). Automatic oscillation detection and characterization in closed-loop systems, Control Engineering Practice 20(8): 733–746.
  • [34] Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.
  • [35] Tatjewski, P. (2010). Supervisory predictive control and on-line set-point optimization, International Journal of Applied Mathematics and Computer Science 20(3): 483–495, DOI: 10.2478/v10006-010-0035-1.
  • [36] Tatjewski, P. (2014). Disturbance modeling and state estimation for offset-free predictive control with state-space process models, International Journal of Applied Mathematics and Computer Science 24(2): 313–323, DOI: 10.2478/amcs-2014-0023.
  • [37] Thornhill, N.F. and Shah, S.L. (2005). New directions in control loop assessment and diagnosis, Computing & Control Engineering Journal 16(4): 18–22.
  • [38] Yang, X. and Maciejowski, J.M. (2015). Fault tolerant control using Gaussian processes and model predictive control, International Journal of Applied Mathematics and Computer Science 25(1): 133–148, DOI: 10.1515/amcs-2015-0010.
  • [39] Zhuo, H. (2009). Research of performance assessment and monitoring for multivariate model predictive control system, 4th International Conference on Computer Science & Education, Nanning, China, pp. 509–514.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5e8ab640-0d18-4091-92c5-7c2cc617b7f1
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