1 LeliĆ, M.A., Zarrop, M.B., “Generalized pole-placement self-tuning controller Part 1, Basic algorithm”, Int. J. Control, 46, 547-568 (1987).2 Clarke, D. W., Advances in Model-Based Predictive Control, chapter Generalized Predictive Control in Clinical Anesthesia, Oxford University Press, Oxford, 189-209 (1994).3 Clarke, D.W., “Application of generalized predictive control to industrial processes”, IEEE Contr. Syst. Mag., 8 (2), 49-55 (1988).4 Richalet, J., “Industrial applications of model based predictive control”, Automatica, 29 (5), 1251-1274 (1993).5 Richalet, J., Abu, E., Arber, C., Kuntze, H.B., Jacubasch, A., Schill, W., “Predictive functional control: Application to fast and accurate robots”, In: The 10th International Federation of Automatic Control (IFAC) Congress, Munich, 251-258 (1987).6 Lakshminarayanan, S., Shah, S.L., Nandakumar, K., “Modeling and control of multivariable processes: Dynamic PLS approach”, AIChE J, 43 (9), 2307-2322 (1997).7 Maciejowski, J.M., Predictive Control: With Constraints, Prentice-Hall, Upper Saddle River, 125-143 (2002).8 Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M., “Constrained model predictive control: Stability and optimality”, Automatica, 36, 789-814 (2000).9 Hovland, S., Gravdahl, J.T., “Complexity reduction in explicit MPC through model reduction”, In: The 17th International Federation of Automatic Control (IFAC) Congress, South Korea, 7711-7716 (2008).10 Wold, S., Martens, H., Wold, H., The Multivariate Calibration Problem in Chemistry Solved by the PLS Method Matrix Pencils, Springer, Berlin, Heidelberg, 286-293(1983).11 Wold, S., Ruhe, A., Wold, H., Dunn, ⅡI W.J., “The collinearity problem in linear regression the partial least squares (PLS) approach to generalized inverses”, SIAM J Sci Stat Comput, 5 (3), 735-743 (1984).12 Chen, J., Hsieh, K.T., Chan, L.L.T., “PLS data-driven based approach to design of a simulated moving bed process”, Sep. Sci. Technol., 65 (2), 173-183 (2009).13 Boulesteix, A.L., “PLS dimension reduction for classification with microarray data”, Stat. Appl. Genet. Mol., 3 (1), 1075-1107 (2004).14 Hu, B., Zheng, P.Y., Liang, J., “Multi-loop internal model controller design based on a dynamic PLS framework”, Chin. J. Chem. Eng., 18 (2), 277-285 (2010).15 Kaspar, M.H., Ray, W.H., “Chemometric methods for process monitoring and high-performance controller design”, AIChE J, 38 (10), 1593-1608 (1992).16 Kaspar, M.H., Harmon, R.W., “Dynamic PLS modelling for process control”, Chem. Eng. Sci., 48 (20), 3447-3461 (1993).17 Chen, J., Cheng, Y.C., Yea, Y., “Multiloop PID controller design using partial least squares decoupling structure”, Korean J. Chem. Eng., 22 (2), 173-183 (2005).18 Zhao, H., Guiver, J., Neelakantan, R., Biegler, L. T., “A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model”, Control Eng. Pract., 9 (2), 125-133 (2001).19 Baffi, G., Morris, J., Martin, E., “Non-linear model based predictive control through dynamic non-linear partial least squares”, Chem. Eng. Res. Des., 80 (1), 75-86 (2002).20 Lauri, D., Salcedo, J., Garcia-Nieto, S., Martinez, M., “A PLS approach to identifying predictive ARX models in Control Applications”, In: Control Applications & Intelligent Control 2009, IEEE, Saint Petersburg, Russia, 1460-1465 (2009).21 Shamekh, A., Lennox, B., Sandoz, D., Marjanovic, O., “PLS and its application within model predictive controllers”, In: The 17th International Federation of Automatic Control (IFAC) Congress, South Korea, 12389-12394 (2008).22 Nguyen, D. V., Rocke, D. M., “On partial least squares dimension reduction for microarray-based classification: a simulation study”, Comput Stat Data An, 46 (3), 407-425 (2004).23 Laksbminarayanan, S., Patwardhan, R. S., “A dynamics PLS framework for constrained model predictive control”, In: The 10th International Federation of Automatic Control (IFAC) Congress, Banff, Alberta, Canada, 482-488 (1997).24 Zhao, Z., Hu, B., Liang, J., “Multi-loop adaptive internal model control based on a dynamic partial least squares model”, J. Zhejiang Univ. Sci. A, 12 (3), 190-200 (2011).25 Trygg, J., Wold, S., “Orthogonal projections to latent structures (O-PLS)”, J. Chemometr, 16 (3), 119-128 (2002).26 Zwick, W. R., Velicer, W. F., “Comparison of five rules for determining the number of components to retain”, Psychol. Bull., 99 (3), 432-442 (1986).27 Qin, S.J., McAvoy, T.J., “Nonlinear PLS modeling using neural networks”, Comput. Chem. Eng., 16 (4), 379-391 (1992).28 Ricker, N.L., “The use of biased least-squares estimators for parameters in discrete-time pulse-response models”, Ind. Eng. Chem. Res., 27 (2), 343-350 (1988).29 Morari, M., Garcia, C.E., Lee, J.H., Prett, D.M., Model Predictive Control, Prentice-Hall, Upper Saddle River, 119-139 (1993).30 Reeves, D.E., Arkun, Y., “Interaction measures for nonsquare decentralized control structures”, AIChE J, 35 (5), 603-613 (1989).31 Kalman, R.E., “Mathematical description of linear dynamical systems”, SIAM J Ser A: Control, 1 (2), 152-192 (1963).32 Hu, S., Automatic control theory, Science Press, Beijing, 35-56 (2001).33 Kokate, R.D., Waghmare, L.M., Deshmukh, S.D., “Review of Tuning Methods of DMC and Performance Evaluation with PID Algorithms on a FOPDT Model”, In: International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, India, 71-75 (2010).34 Embiruçu, M., Fontes, C., “Multirate multivariable generalized predictive control and its application to a slurry reactor for ethylene polymerization”, Chem. Eng. Sci., 61 (17), 5754-5767 (2006) |