Assessment and Future Directions of Nonlinear Model Predictive ...

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Series Advisory BoardF. Allgöwer, P. Fleming, P. Kokotovic,A.B. Kurzhanski, H. Kwakernaak,A. Rantzer, J.N. TsitsiklisEditorsDr.-Ing. Rolf FindeisenInstitute for Systems Theory and AutomaticControlUniversity of StuttgartPfaffenwaldring 970550 StuttgartGermanyE-mail: findeise@ist.uni-stuttgart.deProf. Dr. Lorenz T. BieglerChemical Engineering DepartmentCarnegie Mellon UniversityPittsburgh, PA 15213USAE-mail: lb01@andrew.cmu.eduProf. Dr. Frank AllgöwerInstitute for Systems Theory and AutomaticControlUniversity of StuttgartPfaffenwaldring 970550 StuttgartGermanyE-mail: allgower@ist.uni-stuttgart.deLibrary of Congress Control Number: 2007927163ISSN print edition: 0170-8643ISSN electronic edition: 1610-7411ISBN-10 3-540-72698-5 Springer Berlin Heidelberg New YorkISBN-13 978-3-540-72698-2 Springer Berlin Heidelberg New YorkThis work is subject to copyright. All rights are reserved, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication orparts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, inits current version, and permission for use must always be obtained from Springer. Violations are liable forprosecution under the German Copyright Law.Springer is a part of Springer Science+Business Mediaspringer.comc○ Springer-Verlag Berlin Heidelberg 2007The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply,even in the absence of a specific statement, that such names are exempt from the relevant protective laws andregulations and therefore free for general use.Typesetting: by the authors and SPS using a Springer LATEX macro packagePrinted on acid-free paper SPIN: 12067183 89/SPS 5 43210
PrefaceThe past three decades have seen rapid development in the area of model predictivecontrol with respect to both theoretical and application aspects. Over these30 years, model predictive control for linear systems has been widely applied,especially in the area of process control. However, today’s applications oftenrequire driving the process over a wide region and close to the boundaries of operability,while satisfying constraints and achieving near-optimal performance.Consequently, the application of linear control methods does not always lead tosatisfactory performance, and here nonlinear methods must be employed. Thisis one of the reasons why nonlinear model predictive control (NMPC) has enjoyedsignificant attention over the past years, with a number of recent advanceson both the theoretical and application frontier. Additionally, the widespreadavailability and steadily increasing power of today’s computers, as well as thedevelopment of specially tailored numerical solution methods for NMPC, bringthe practical applicability of NMPC within reach even for very fast systems. Thishas led to a series of new, exciting developments, along with new challenges inthe area of NMPC.In order to summarize these recent developments, and to consider these newchallenges, we organized an international workshop entitled “Assesment and FutureDirections of Nonlinear Model Predictive Control” (NMPC05), which washeld at the Waldhotel Zollernblick, in Freudenstadt-Lauterbad, Germany on August26-30, 2005. The objective of this workshop was to bring together a diversegroup of internationally recognized researchers and industrial practitioners inthe area of NMPC, in order to critically assess and discuss the current status,future directions and open questions of NMPC. The number of participants wasintentionally kept small in order to promote discussions and the fruitful exchangeof ideas. In the spirit of the very successful predecessor workshop held in 1998in Ascona, Switzerland, all the keynotes, as well as the main talks were givenby invited speakers. There were also a limited number of contributed oral andposter presentations. Overall the workshop turned out to be very stimulatingand allowed close interactions and discussions among the participants.
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Page 7 and 8: ContentsFoundations and History of
Page 9 and 10: ContentsIXRobustness, Robust Design
Page 11 and 12: ContentsXIHybrid NMPC Control of a
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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 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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Nonlinear Model Predictive Control:
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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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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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