Assessment and Future Directions of Nonlinear Model Predictive ...

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380 R.D. BartusiakOperators and process engineers must have sufficient understanding and trustin the NLMPC application for it to be sustained at full effectiveness. This need isparticularly acute when the NLMPC is controlling a unit where only SISO controlsexisted before. Pre-commissioning training; timely, process-relevant explanationsof controller behavior; and what-if simulation studies are some measuresthat can increase understanding and trust.Closing RemarksNLMPC is practical in industrial service. We have no doubt that the processcontrol community – academics, vendors, and operating companies – will continuouslyimprove on the technology described or surveyed in this paper. Dogmaon the choice between fundamental and empirical models used across-the-boardfor NLMPC is doomed to be erroneous. NLMPC is set to be the platform for optimalcontrol of feed- or product flexible manufacturing processes characterizedby significant nonlinearities.AcknowledgementsThe principal contributors to the work described in this paper include Robert W.Fontaine, Carl O. Schwanke, and Arul R. Gomatam. Other contributors to Exxon-Mobil Chemicals’ NLMPC work are listed in [28]. The author is grateful to WilliamF. Floyd for comments that helped converge the topics to be included in this paper,and to Christopher E. Long for highlighting points requiring clarification.References[1] Choi KY, Ray WH (1985) Chem Eng Sci (1985) 40:2261-2279.[2] Peterson T, Hernandez E, Arkun Y, Schork J (1992) Chem Eng Sci. 47:737-753.[3] Bartusiak RD, Georgakis CG, Reilly MJ (1988) Chem Eng Sci 44:1837-1851.[4] Li WC, Biegler LT (1988) Ind & Eng Chem Res. 27:1421-1433.[5] Bartusiak RD, Fontaine RW (1997) Feedback method for controlling nonlinearprocesses. U.S. Patent 5,682,309.[6] Bequette BW (1991) Ind Eng Chem Res 30:1391-1413.[7] Findeisen RH, Allgower F(2000) Nonlinear model predictive control for index-oneDAE systems. In: Allgower F, Zheng A (eds) Nonlinear Model Predictive Control.Birkhauser Verlag, Basel.[8] Qin SJ, Badgwell TA (2000) An overview of nonlinear model predictive controlapplications. In: Allgower F, Zheng A (eds) Nonlinear Model Predictive Control.Birkhauser Verlag, Basel.[9] Qin SJ, Badgwell TA (2003) Control Eng Practice 11:733-764.[10] Saffer DR, Doyle FJ (2004) Comp & Chem Eng. 41:2749-2793.[11] Snow WP, Emigholz KF, Zhu Y (2001) Petroleum Technology Quarterly. 02:97-101.[12] Martinsen F, Biegler LT, Foss BA (2004) J Proc Control. 14:853-865.[13] Lee JH, Morari M, Garcia CE (1994) Automatica. 30:707-717.
NLMPC: A Platform for Optimal Control 381[14] Lundstrom P, Lee JH, Morari M, Skogestad S (1995) Comp & Chem Eng. 19:409-421.[15] Robertson DG, Lee JH (1995) J Proc Cont 5:291-299.[16] Rawlings JB, Muske KR (1993) IEEE Trans Auto Control. 38:1512-1516.[17] Young RE, Bartusiak RD, Fontaine RW (2002). Evolution of an industrial nonlinearmodel predictive controller. In: Rawlings JB, Ogunnaike BA, Eaton JW (eds)Sixth International Conference on Chemical Process Control. AIChE SymposiumSeries No. 326 Vol. 98. CACHE, AIChE.[18] Harmse M, Hokanson DA (2005) Experiences in using SmartStep. AspenTechAC&O User Group meeting, League City TX.[19] Bindlish R, Rawlings JB (2003) AIChE J 49:2885-2899.[20] Helbig A, Abel O, Marquardt W (2000) Structural concepts for optimizationbased control of transient processes. In: Allgower F, Zheng A (eds) NonlinearModel Predictive Control. Birkhauser Verlag, Basel.[21] Astrom KJ, Wittenmark B (1984) Computer Controlled Systems Theory andDesign. Prentice-Hall, Englewood Cliffs.[22] Grossmann I (2005) AIChE J. 51:1846-1857.[23] Gallun SE, Luecke RH, Scott DE Morshedi AM (1992) Hydrocarbon Proc. 71:78-82.[24] Lee JH (2000) Modeling and identification in nonlinear model predictive control:Requirements, current status and future research needs. In: Allgower F, Zheng A(eds) Nonlinear Model Predictive Control Birkhauser Verlag, Basel.[25] Vragolic S (2004) Alternate methods of process model identification: Benzenedistillation. Proceedings of the 2004 Ethylene Producers Conference, AIChE.[26] Vogel EF, Downs JJ (2002). Industrial experience with state space model predictivecontrol. In: Rawlings JB, Ogunnaike BA, Eaton JW (eds) Sixth InternationalConference on Chemical Process Control. AIChE Symposium Series No. 326 Vol.98. CACHE, AIChE.[27] Heseltine EL, Rawlings JB (2005) Ind & Eng Chem Res. 44:2451-2460.[28] Bartusiak RD, Fontaine RW, Schwanke CO, Gomatam AR (2003) Nonlinearmodel predictive control of polymerization reactors. AIChE Annual Meeting, SanFrancisco CA.
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Lecture Notesin Control and Informa
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Series Advisory BoardF. Allgöwer,
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ContentsFoundations and History of
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ContentsIXRobustness, Robust Design
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ContentsXIHybrid NMPC Control of a
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Nonlinear Model Predictive Control:
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Hybrid MPC: Open-Minded but Not Eas
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Hybrid MPC: Open-Minded but Not Eas
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22 S.E. Tuna et al.this says that i
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24 S.E. Tuna et al.In particular, f
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26 S.E. Tuna et al.W N (f(x, ¯κ N
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28 S.E. Tuna et al.where ᾱ := max
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30 S.E. Tuna et al.21.521.5µ=51
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32 S.E. Tuna et al.obstacle and gra
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34 S.E. Tuna et al.[19] C. Prieur.
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36 É. Gyurkovics and A.M. Elaiw[8]
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38 É. Gyurkovics and A.M. ElaiwWe
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40 É. Gyurkovics and A.M. ElaiwWe
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Conditions for MPC Based Stabilizat
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A Computationally Efficient Schedul
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The Potential of Interpolation for
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The Potential of Interpolation 65De
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The Potential of Interpolation 67Al
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The Potential of Interpolation 693.
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The Potential of Interpolation 71Pr
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The Potential of Interpolation 73Th
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The Potential of Interpolation 75de
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Techniques for Uniting Lyapunov-Bas
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94 M. Lazar et al.Lipschitz continu
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96 M. Lazar et al.let X T ⊆ X den
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98 M. Lazar et al.S 0 {j ∈S|0
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100 M. Lazar et al.Fig. 1. State-sp
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102 M. Lazar et al.[11] Grimm, G.,
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Model Predictive Control for Nonlin
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ReferencesModel Predictive Control
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116 F.A.C.C. Fontes, L. Magni, and
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On the Computation of Robust Contro
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142 M. Srinivasarao, S.C. Patwardha
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Numerical Methods for Efficient and
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L i (s i ,u i ):=Numerical Methods
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182 A. Grancharova, T.A. Johansen,
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194 V. Sakizlis et al.presented her
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196 V. Sakizlis et al.linear PWA fu
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198 V. Sakizlis et al.non-linear sy
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200 V. Sakizlis et al.Remark 1. For
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202 V. Sakizlis et al.One should no
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204 V. Sakizlis et al.g0.050.10.15x
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Interior-Point Algorithms for Nonli
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n p,2 ≤Interior-Point Algorithms
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Hard Constraints for Prioritized Ob
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A Nonlinear Model Predictive Contro
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Robustness and Robust Design of MPC
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MPC for Stochastic SystemsMark Cann
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y i (k) =∑n um=1MPC for Stochasti
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MPC for Stochastic Systems 259we ha
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MPC for Stochastic Systems 261form
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MPC for Stochastic Systems 263( )κ
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MPC for Stochastic Systems 265Fig.
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MPC for Stochastic Systems 267⎡
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NMPC for Complex Stochastic Systems
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284 H. Chen et al.of the moving hor
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286 H. Chen et al.Hence, at each sa
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288 H. Chen et al.control inputs in
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290 H. Chen et al.Corollary 1. The
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292 H. Chen et al.c A[mol/l]10−10
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294 H. Chen et al.[CSA97] H.Chen,C.
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296 L. Xie, P. Li, and G. Woznyand
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298 L. Xie, P. Li, and G. WoznyDeta
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300 L. Xie, P. Li, and G. Wozny3.2
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302 L. Xie, P. Li, and G. WoznyFig.
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304 L. Xie, P. Li, and G. Wozny[3]
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306 H. Arellano-Garcia et al.applic
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308 H. Arellano-Garcia et al.Fig. 1
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310 H. Arellano-Garcia et al.RECIPE
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312 H. Arellano-Garcia et al.optimi
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314 H. Arellano-Garcia et al.The re
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Interval Arithmetic in Robust Nonli
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Optimal Online Control of Dynamical
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Integration of Economical Optimizat
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Controlling Distributed Hyperbolic
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444 K.R. Muske, A.E. Witmer, and R.
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Robust NMPC for a Benchmark Fed-Bat
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Real-Time Implementation of Nonline
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Non-linear Model Predictive Control
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NMPC of the Hashimoto Simulated Mov
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486 R. Lepore et al.In this study,
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488 R. Lepore et al.3 Control Strat
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490 R. Lepore et al.400.80.6|x−x
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492 R. Lepore et al.0.950.9w P;30.8
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Hybrid NMPC Control of a Sugar Hous
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Application of the NEPSAC Nonlinear
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Integrating Fault Diagnosis with No
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524 M. Alamirmin V (x u(·,x),T) un
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526 M. AlamirDefinition 4. The syst
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528 M. Alamir♭V is radially unbou
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530 M. AlamirBut according to the s
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532 M. AlamirFig. 2. Stabilization
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534 M. AlamirFig. 3. Closed loop be
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A New Real-Time Method for Nonlinea
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A Two-Time-Scale Control Scheme for
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566 X.-B. Hu and W.-H. Chen2 Online
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568 X.-B. Hu and W.-H. Chentime alo
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570 X.-B. Hu and W.-H. ChenFig. 3.
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572 X.-B. Hu and W.-H. ChenTable 4.
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574 M. Fujita et al. CameraCameraTa
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576 M. Fujita et al.J(u, t) =∫t+T
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578 M. Fujita et al.ξ 2[rad/s]6420
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580 M. Fujita et al.Acknowledgement
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582 A. Casavola, D. Famularo, and G
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4. c(t) ∈Cfor all t ∈ Z + ;5. T
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608 W.B. Dunbar and S. Desaand sequ
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610 W.B. Dunbar and S. Desademand r
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612 W.B. Dunbar and S. Desabacklog
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614 W.B. Dunbar and S. Desacasescas
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Robust Model Predictive Control for
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630 J.J. Arrieta-Camacho, L.T. Bieg
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Author IndexAlamir, Mazen 523Alamo,
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Lecture Notes in Control and Inform
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