Assessment and Future Directions of Nonlinear Model Predictive ...

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Putting Nonlinear Model Predictive Control into Use 417[5] K. Grjotheim and H. Kvande, eds., ”Introduction to aluminium electrolysis”.Aluminum-Verlag, 1993.[6] W.C.LiandL.T.Biegler,”Multistep,Newton-typecontrolstrategiesforconstrainednonliner processes”, Chem. Eng. Res. Des., vol.67, pp. 562-577, 1989.[7] N. M. C. de Oliveria and L. T. Biegler, “Constraint handling and stability propertiesof model-predictive control,” AIChE Journal, vol. 40, pp. 1138-1155, 1994.[8] N. M. C. de Oliveria and L. T. Biegler, ”A finite-difference method for linearizationin nonlinear estimation algorithms”, Automatica, vol.31, pp. 281-286, 1995.[9] S. J. Qin and T. A. Badgwell, ”An Overview of Nonlinear Model PredictiveControl”, In: Nonlinear Model Predictive Control, F. Allgower and A. ZhengBirkhauser, eds., 2000.[10] C. V. Rao and J. B. Rawlings, ”Nonlinear Moving Horizon Estimation”, In: NonlinearModel Predictive Control, F. Allgower and A. Zheng Birkhauser, eds., 2000.
Integration of Economical Optimization andControl for Intentionally Transient ProcessOperationJitendra V. Kadam ⋆ and Wolfgang MarquardtLehrstuhl für Prozesstechnik, RWTH Aachen University, Turmstr. 46, D-52064Aachen, Germany⋆ ExxonMobil Chemical Company, Canadastraat 20, B-2070 Zwijndrecht, Belgium{kadam,marquardt}@lpt.rwth-aachen.deSummary. This paper summarizes recent developments and applications of dynamicreal-time optimization (D-RTO). A decomposition strategy is presented to separateeconomical and control objectives by formulating two subproblems in closed-loop. Twoapproaches (model-based and model-free at the implementation level) are developedto provide tight integration of economical optimization and control, and to handleuncertainty. Simulated industrial applications involving different dynamic operationalscenarios demonstrate significant economical benefits.1 IntroductionIncreasing competition coupled with a highly dynamic economic environment inthe process industry require a more agile plant operation in order to increaseproductivity under flexible operating conditions while decreasing the overall productioncost [1]. The polymer industry is an illustrative example of this development.While on the one hand the product specifications for high-value productsbecome tighter and tighter, on the other hand many of the specialty polymersare becoming commodities resulting in lower profit margins, thus requiring an efficientand cost-effective production [6]. Multi-product and multi-purpose plantshave become common. Therefore, transient operational tasks involving suddenchanges in production load, product grade (usually triggered by market conditions)are routinely performed. These scenarios demand integrated economicaloptimization of the overall plant operation.Today’s plant operation requires real-time business decision making (RT-BDM) tasks at different levels integrating planning, scheduling, optimizationand control tasks. Figure 1 depicts a typical decision making and automationhierarchy. Due to a wide range of process dynamics, different time-scales areinvolved at each level such as fractions of seconds for base layer control, minutesfor advanced control, hours for set-point/trajectory optimization, days for planningand scheduling, and months or even years for strategic corporate planning.⋆ Current address.R. Findeisen et al. (Eds.): Assessment and Future Directions, LNCIS 358, pp. 419–434, 2007.springerlink.com c○ Springer-Verlag Berlin Heidelberg 2007
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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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Page 367 and 368: New Extended Kalman Filter Algorith
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Page 385 and 386: 384 K. Naidoo et al.polymer control
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Page 389 and 390: 388 K. Naidoo et al.1. It greatly s
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Page 395 and 396: 394 K. Naidoo et al.1. Maintain pro
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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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Distributed Model Predictive Contro
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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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