Assessment and Future Directions of Nonlinear Model Predictive ...

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604 A.N. Venkat, J.B. Rawlings, and S.J. Wrightsystemwide control performance (centralized control) at convergence. Further,all intermediate iterates are feasible and the resulting distributed control lawguarantees closed-loop stability under state and output feedback. Several exampleswere presented to illustrate feasibility, nominal stability and performanceperformance properties of the proposed distributed MPC framework.AcknowledgmentThe authors gratefully acknowledge the financial support of the industrialmembers of the Texas-Wisconsin Modeling and Control Consortium, and NSFthrough grant #CTS-0456694.References[1] J.G.V. Antwerp and R.D. Braatz. Model predictive control of large scale processes.J. Proc. Control, 10:1–8, 2000.[2] Eduardo Camponogara, Dong Jia, Bruce H. Krogh, and Sarosh Talukdar. Distributedmodel predictive control. IEEE Ctl. Sys. Mag., pages 44–52, February2002.[3] H. Cui and E.W. Jacobsen. Performance limitations in decentralized control. J.Proc. Control, 12:485–494, 2002.[4] V. Havlena and J. Lu. A distributed automation framework for plant-wide control,optimisation, scheduling and planning. In Proceedings of the 16th IFAC WorldCongress, Prague, Czech Republic, July 2005.[5] Yu-Chi Ho. On Centralized Optimal Control. IEEE Trans. Auto. Cont., 50(4):537–538, 2005.[6] Dong Jia and Bruce H. Krogh. Distributed model predictive control. In Proceedingsof the American Control Conference, Arlington, Virginia, June 2001.[7] Rudolf Kulhavý, Joseph Lu, and Tariq Samad. Emerging technologies for enterpriseoptimization in the process industries. In James B. Rawlings, Babatunde A.Ogunnaike, and John W. Eaton, editors, Chemical Process Control–VI: Sixth InternationalConference on Chemical Process Control, pages 352–363, Tucson, Arizona,January 2001. AIChE Symposium Series, Volume 98, Number 326.[8] Truls Larsson and Sigurd Skogestad. Plantwide control-a review and a new designprocedure. Mod. Ident. Control, 21(4):209–240, 2000.[9] J.Z. Lu. Challenging control problems and emerging technologies in enterpriseoptimization. Control Eng. Prac., 11(8):847–858, August 2003.[10] J. Lunze. Feedback Control of Large Scale Systems. Prentice-Hall, London, U.K,1992.[11] T.A. Meadowcroft, G. Stephanopoulos, and C. Brosilow. The Modular MultivariableController: 1: Steady-state properties. AIChE J., 38(8):1254–1278, 1992.[12] Babatunde A. Ogunnaike and W. Harmon Ray. Process Dynamics, Modeling, andControl. Oxford University Press, New York, 1994.[13] S. Joe Qin and Thomas A. Badgwell. A survey of industrial model predictivecontrol technology. Control Eng. Prac., 11(7):733–764, 2003.[14]B.Ramachandran,J.B.Riggs,H.R.Heichelheim,A.F.Seibert,andJ.R.Fair.Dynamic simulation of a supercritical fluid extraction process. Ind. Eng. Chem.Res., 31:281–290, 1992.
Distributed Model Predictive Control of Large-Scale Systems 605[15] Yudi Samyudia, Peter L. Lee, Ian T. Cameron, and Michael Green. Control strategiesfor a supercritical fluid extraction process. Chem. Eng. Sci., 51(5):769–787,1996.[16] Nils R. Sandell-Jr., Pravin Varaiya, Michael Athans, and Michael Safonov. Surveyof decentralized control methods for larger scale systems. IEEE Trans. Auto.Cont., 23(2):108–128, 1978.[17] D.D. Siljak. Decentralized Control of Complex Systems. Academic Press, London,1991.[18] S. Skogestad and M. Morari. Implications of large RGA elements on controlperformance. Ind. Eng. Chem. Res., 26:2323–2330, 1987.[19] Aswin N. Venkat, James B. Rawlings, and Stephen J. Wright. Stability andoptimality of distributed model predictive control. In Proceedings of the Joint44th IEEE Conference on Decision and Control and European Control Conference,Seville, Spain, December 2005.[20] Robert E. Young, R. Donald Bartusiak, and Robert W. Fontaine. Evolutionof an industrial nonlinear model predictive controller. In James B. Rawlings,Babatunde A. Ogunnaike, and John W. Eaton, editors, Chemical Process Control–VI: Sixth International Conference on Chemical Process Control, pages 342–351,Tucson, Arizona, January 2001. AIChE Symposium Series, Volume 98, Number326.[21] G. Zhu and M.A. Henson. Model predictive control of interconnected linear andnonlinear processes. Ind. Eng. Chem. Res., 41:801–816, 2002.
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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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State Estimation Analysed as Invers
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Minimum-Distance Receding-Horizon S
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New Extended Kalman Filter Algorith
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NLMPC: A Platform for Optimal Contr
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384 K. Naidoo et al.polymer control
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386 K. Naidoo et al.are two key qua
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388 K. Naidoo et al.1. It greatly s
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390 K. Naidoo et al.However, with t
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392 K. Naidoo et al.Fig. 4. Bounded
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394 K. Naidoo et al.1. Maintain pro
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396 K. Naidoo et al.7 Commissioning
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398 K. Naidoo et al.[3] N.M.C. Oliv
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400 R. Franke and J. DoppelhamerImp
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402 R. Franke and J. DoppelhamerFig
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404 R. Franke and J. DoppelhamerFig
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406 R. Franke and J. Doppelhamer[3]
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408 B.A. Foss and T.S. Scheicritica
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410 B.A. Foss and T.S. ScheiFig. 2.
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412 B.A. Foss and T.S. Scheidiscuss
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414 B.A. Foss and T.S. ScheiOther a
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416 B.A. Foss and T.S. ScheiIn the
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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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Page 545 and 546: A Two-Time-Scale Control Scheme for
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