Assessment of the GPC Control Quality Using Non–Gaussian Statistical Measures

Anderson, T.W. and Darling, D.A. (1954). A test of goodness-of-fit, Journal of the American Statistical Association 49(268): 765-769.10.1080/01621459.1954.10501232Search in Google Scholar

Åström, K.J. (1967). Computer control of a paper machine-an application of linear stochastic control theory, IBMJournal 11(4): 389-405.10.1147/rd.114.0389Search in Google Scholar

Axensten, P. (2006). Cauchy CDF, PDF, inverse CDF, parameter fit and random generator, http://www.mathworks.com/matlabcentral/fileexchange/11749-cauchy/.Search in Google Scholar

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. 21-56.10.1007/978-3-642-18062-0_1Search in Google Scholar

Camacho, E.F. and Bordons, C. (1999). Model Predictive Control, Springer, London.10.1007/978-1-4471-3398-8Search in Google Scholar

Choudhury, M.A.A.S., Shah, S. and Thornhill, N. (2008). Diagnosis of Process Nonlinearities and Valve Stiction, Springer, Heidelberg.Search in Google Scholar

Clarke, W. and Mohtadi, C. (1989). Properties of generalized predictive control, Automatica 25(5): 859-875.10.1016/0005-1098(89)90053-8Search in Google Scholar

Clarke, W., Mohtadi, C. and Tuffs, P.S. (1987a). Generalized predictive control. I: The basic algorithm, Automatica 23(2): 137-148.10.1016/0005-1098(87)90087-2Search in Google Scholar

Clarke, W., Mohtadi, C. and Tuffs, P.S. (1987b). Generalized predictive control. II: Extensions and interpretations, Automatica 23(2): 149-160.10.1016/0005-1098(87)90088-4Search in Google Scholar

Cramer, H. (1928). On the composition of elementary errors, Scandinavian Actuarial Journal 1928(1): 13-74.10.1080/03461238.1928.10416862Search in Google Scholar

Cross, R. and Iqbal, A. (1995). The Rank Xerox experience: Benchmarking ten years on, in A. Rolstad°as (Ed.), Benchmarking-Theory and Practice, Springer US, Boston, MA, pp. 3-10.10.1007/978-0-387-34847-6_1Search in Google Scholar

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.Search in Google Scholar

Domański, P.D. (2016). Non-Gaussian and persistence measures for control loop quality assessment, Chaos: An Interdisciplinary Journal of Nonlinear Science 26(4): 043105.10.1063/1.494677227131484Search in Google Scholar

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.Search in Google Scholar

Harris, T. (1989). Assessment of closed loop performance, Canadian Journal of Chemical Engineering 67(5): 856-861.10.1002/cjce.5450670519Search in Google Scholar

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): 180-189.10.2307/2346692Search in Google Scholar

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.Search in Google Scholar

Hugo, A.J. (2006). Performance assessment of single-loop industrial controllers, Journal of Process Control 16(8): 785-794.10.1016/j.jprocont.2006.03.004Search in Google Scholar

Jelali, M. (2006). An overview of control performance assessment technology and industrial applications 14(5): 441-466.10.1016/j.conengprac.2005.11.005Search in Google Scholar

Jelali, M. (2013). Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance, Springer-Verlag, London.10.1007/978-1-4471-4546-2Search in Google Scholar

Koutrouvelis, I.A. (1980). Regression-type estimation of the parameters of stable laws, Journal of the American Statistical Association 75(372): 918-928.10.1080/01621459.1980.10477573Search in Google Scholar

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-2015-0060.10.1515/amcs-2015-0060Search in Google Scholar

Lilliefors, H. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown, Journal of the American Statistical Association 62(318): 399-402.10.1080/01621459.1967.10482916Search in Google Scholar

Maciejowski, J.M. (2002). Predictive Control with Constraints, Prentice Hall, Harlow.Search in Google Scholar

Ordys, A., Uduehi, D. and Johnson, M.A. (2007). Process Control Performance Assessment-From Theory to Implementation, Springer, London.10.1007/978-1-84628-624-7Search in Google Scholar

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): 155-168.10.1016/S0959-1524(02)00018-5Search in Google Scholar

Schäfer, J. and Cinar, A. (2004). Multivariable MPC system performance assessment, monitoring, and diagnosis, Journal of Process Control 14(2): 113-129.10.1016/j.jprocont.2003.07.003Search in Google Scholar

Seborg, D.E., Mellichamp, D.A., Edgar, T.F. and Doyle, F.J. (2010). Process Dynamics and Control, Wiley, Hoboken, NJ.Search in Google Scholar

Shapiro, S.S. and Wilk, M.B. (1965). An analysis of variance test for normality (complete samples), Biometrika 52(3-4): 591-611.10.1093/biomet/52.3-4.591Search in Google Scholar

Shinskey, F.G. (2002). Process control: As taught vs. as practiced, Industrial and Engineering Chemistry Research 41(16): 3745-3750.Search in Google Scholar

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.Search in Google Scholar

Spinner, T., Srinivasan, B. and Rengaswamy, R. (2014). Data-based automated diagnosis and iterative retuning of proportional-integral (PI) controllers, Control Engineering Practice 29: 23-41.10.1016/j.conengprac.2014.03.005Search in Google Scholar

Srinivasan, B. and Rengaswamy, R. (2012). Automatic oscillation detection and characterization in closed-loop systems, Control Engineering Practice 20(8): 733-746.10.1016/j.conengprac.2012.02.008Search in Google Scholar

Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.Search in Google Scholar

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/v10006-010-0035-1.10.2478/v10006-010-0035-1Search in Google Scholar

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-2014-0023.10.2478/amcs-2014-0023Search in Google Scholar

Thornhill, N.F. and Shah, S.L. (2005). New directions in control loop assessment and diagnosis, Computing & Control Engineering Journal 16(4): 18-22.10.1049/cce:20050404Search in Google Scholar

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-2015-0010.10.1515/amcs-2015-0010Search in Google Scholar

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.Search in Google Scholar