Nonlinear model predictive controller

P. Kittisupakom, Ph.D. thesis, The use of nonlinear model predictive control techniques for the control of a reactor with exothermic reactions, University of London, 1995. 

L. Magni, D.M. Raimondo, and F. Allgower, editors, Nonlinear Model Predictive Control. Springer, Berlin, 2009. 

Z.K. Nagy and R.D. Braatz. Robust nonlinear model predictive control of batch processes. AIChE Journal, 49 1776-1786, 2003. 

A. A. Patwardhan, J.B. Rawlings, and T.F. Edgar. Nonlinear model predictive control. Chemical Engineering Communications, 87 1-23, 1990. 

H. Seki, M. Ogawa, S. Ooyama, K. Akamatsu, M. Ohshima, and W. Yang. Industrial application of a nonlinear model predictive control to polymerization reactors. Control Engineering... 

R. K. A1 Seyab and Y. Cao, Nonlinear model predictive control of the ALSTOM gasifier, J. Process Control, 16, 795-808 (2006). 

Wave models were successfully used for the design of a supervisory control system for automatic start-up of the coupled column system shown in Fig. 5.15 [19] and for model-based measurement and online optimization of distillation columns using nonlinear model predictive control [15], The approach was also extended to reactive distillation processes by using transformed concentration variables [22], However, in reactive - as in nonreactive - distillation, the approach applies only to processes with constant pattern waves, which must be checked first. 

S. Griiner, S. Schwarzkopf, I. Uslu, et ah, Nonlinear model predictive control of multicomponent distillation columns using wave models. Proceedings, jth International Symposium on Advanced Control of Chemical Processes. Vol. 1,... 

Baldea, M., Daoutidis, P., and Nagy, Z.K. (2010). Nonlinear Model Predictive Control of integrated process systems. In Proceedings Nonlinear Control Systems (NOLCOS 2010). 

Ricker, N. L. and Lee, J. H. (1995). Nonlinear model predictive control of the Tennessee Eastman challenge process. Cornput. Chem. Eng., 19, 961. 

Very few unbiased publications have appeared in the literature comparing control effectiveness using MPC versus a well-designed conventional control system. Most of the MPC applications reported have considered fairly simple processes with a small number of manipulated variables. There are no published reports that discuss the application of MPC to an entire complex chemical plant, with one notable exception. That is the work of Ricker (1996), who compared MPC with conventional PI control for the Eastman process (TE problem). His conclusion was there appears to be little, if any, advantage to the use of nonlinear model predictive control (NMPC) in this application. In particular, the decentralized strategy does a better job of handling constraints—an area in which NMPC is reputed to excel.51... 

Genceli, H. and Nikolaou, M., Design of robust constrained nonlinear model predictive controllers with Volterra series, AIChE J. 41, 9, 2098-2107 (1995). 

Mayne, D. Q., Nonlinear model predictive control An assessment, in Fifth International Conference on Chemical Process Control (Kantor, J. C., Garcia, C. E., and Carnahan, B., Eds.), AIChE Symposium Series, Vol. 93, pp. 217-231 (1997). 

Rawlings, J. B., Meadows, E. S., and Muske, K. R., Nonlinear model predictive control A tutorial and survey. Proceedings of ADCHEM 94, 203-214, Kyoto, Japan (1994). 

Nonlinear model predictive control of a swelling constrained industrial batch reactor... 

Nonlinear Model Predictive Control of a Swelling Constrained Industrial Batch Reactor 527... 

F. Allgower, R. Findeisen, Z. K. Nagy, (2004), Nonlinear model predictive control From theory to application. Journal of the Chinese Institute of Chemical Engineers, 35, 299. 

Z. K. Nagy, R. D. Braatz, (2003), Robust Nonlinear Model Predictive Control of Batch Processes, AlChE Journal, 49, 1776. 

Design of constrained nonlinear model predictive control based on global optimisation... 

Mayne, D. Nonlinear model predictive control challenges and opportunities, in Progr. Systems Control Theor., Allgower, F., Zheng, A. (Eds), 2000, 26, 23-44. 

Jeong, B.G. Yoo, K.Y. Rhee, H.K. Nonlinear model predictive control using a Wiener model of a continuous methyl methacrylate polymerization reactor. Ind. Eng. Chem. Res. 2001, 40 (25), 5968-5977. 

Young, R.E. Bartusiak, R.D. Fontaine, R.W. Evolution of an industrial nonlinear model predictive controller. AIChE Symp. Ser. 2002, 98, 342-351. 

B Maner, FJ Doyle III, B Ogunnaike, and R Pearson. Nonlinear model predictive control of a multivariable polymerization reactor using second-order Volterra series. Automatica, 32 1285-1302, 1996. 

No degree of sophistication in the control system (from adaptive control, to expert systems, to Kalman filters, to nonlinear model predictive control) will work if you do not know how your process works. Many people have tried to use complex controllers to overcome ignorance about the process fundamentals, and they have failed Learn how the process works before you start designing its control system. 

A third model-based approach is neural predictive control, which is a neural network version of nonlinear model predictive control [Trajanoski and Wach, 1998]. In this approach, the neural network is used for ofif-Hne identification of a system model, which is then used to design a nonhnear model predictive controller. This design may provide suitable control of nonlinear systems with time-delays and thus maybe particularly useful in biomedical appHcations. Recent computer simulation studies have demonstrated positive results for control of insuhn dehvery [Trajanoski and Wach, 1998]. 

Dones, I., Manenti, F., Preisig, HA., and Buzzi-Ferraris, G. (2010) Nonlinear model predictive control a self-adaptive approach. Ind. Eng. Chem. Res., 49, 4782-4791. 

Manenti, F. (2011) Considerations on nonlinear model predictive control techniques. Comput. Chem, Eng., 35, 2491-2509. 

In particular, nonlinear model predictive control and dynamic real-time optimization are recent online applications of dynamic optimization. 

Taking into account that the target variable, the moisture content of the product, is not available for direct measurement, a NN based state observer is proposed for its estimation. The data provided by the NN state observer is used for feedback nonlinear model predictive control of the product moisture content, as shown in figure 4. 

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