Assessment and Future Directions of Nonlinear Model Predictive ...

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178 H.G. Bock et al.iteration scheme and its variants to a multi level real-time iteration whichemploys the variants A, B, C and the standard real-time iteration scheme ina hierarchical fashion. This algorithm aims at combining good local convergenceproperties with short sampling times and is subject of future investigation.Acknowledgments. The authors gratefully acknowledge support by DFG grantBO864/10-1.References[1] F. Allgöwer, T.A. Badgwell, J.S. Qin, J.B. Rawlings, and S.J. Wright. Nonlinearpredictive control and moving horizon estimation – An introductory overview. InP. M. Frank, editor, Advances in Control, Highlights of ECC’99, pages 391–449.Springer, 1999.[2] H.G. Bock, M. Diehl, E.A. Kostina, and J.P. Schlöder. Constrained optimal feedbackcontrol of systems governed by large differential algebraic equations. InL. Biegler, O. Ghattas, M. Heinkenschloss, D. Keyes, and B. van Bloemen Waanders,editors, Real-Time and Online PDE-Constrained Optimization. SIAM, 2005.(in print).[3] H.G. Bock, M. Diehl, D.B. Leineweber, and J.P. Schlöder. A direct multiple shootingmethod for real-time optimization of nonlinear DAE processes. In F. Allgöwerand A. Zheng, editors, Nonlinear Predictive Control, volume26ofProgress in SystemsTheory, pages 246–267, Basel, 2000. Birkhäuser.[4] H.G. Bock, E. Eich, and J.P. Schlöder. Numerical solution of constrainedleast squares boundary value problems in differential-algebraic equations. InK. Strehmel, editor, Numerical Treatment of Differential Equations. Teubner,Leipzig, 1988.[5] H.G. Bock and K.J. Plitt. A multiple shooting algorithm for direct solution ofoptimal control problems. In Proc. 9th IFAC World Congress Budapest, pages243–247. Pergamon Press, 1984.[6] H. Chen and F. Allgöwer. A quasi-infinite horizon nonlinear model predictivecontrol scheme with guaranteed stability. Automatica, 34(10):1205–1218, 1998.[7] G. De Nicolao, L. Magni, and R. Scattolini. Stability and robustness of nonlinearreceding horizon control. In F. Allgöwer and A. Zheng, editors, Nonlinear PredictiveControl, volume26ofProgress in Systems Theory, pages 3–23, Basel, 2000.Birkhäuser.[8] M. Diehl. Real-Time Optimization for Large Scale Nonlinear Processes, volume920 of Fortschr.-Ber. VDI Reihe 8, Mess-, Steuerungs- und Regelungstechnik. VDIVerlag, Düsseldorf, 2002. http://www.ub.uni-heidelberg.de/archiv/1659/.[9] M. Diehl, H.G. Bock, and J.P. Schlöder. A real-time iteration scheme for nonlinearoptimization in optimal feedback control. SIAM J. Control Optim., 43(5):1714–1736, 2005.[10] M. Diehl, H.G. Bock, J.P. Schlöder,R.Findeisen,Z.Nagy,andF.Allgöwer. Realtimeoptimization and nonlinear model predictive control of processes governedby differential-algebraic equations. J. Proc. Contr., 12(4):577–585, 2002.[11] M. Diehl, R. Findeisen, and F. Allgöwer. A stabilizing real-time implementation ofnonlinear model predictive control. In L. Biegler, O. Ghattas, M. Heinkenschloss,D. Keyes, and B. van Bloemen Waanders, editors, Real-Time and Online PDE-Constrained Optimization. SIAM, 2004. (in print).
Numerical Methods for Efficient and Fast NMPC 179[12] M. Diehl, R. Findeisen, F. Allgöwer, H.G. Bock, and J.P. Schlöder. Nominalstability of the real-time iteration scheme for nonlinear model predictive control.IEE Proc.-Control Theory Appl., 152(3):296–308, May 2005.[13] M. Diehl, A. Walther, H.G. Bock, and E. Kostina. An adjoint-based SQP algorithmwith quasi-Newton Jacobian updates for inequality constrained optimization.Technical Report MATH-WR 02-2005, Technical University Dresden, Germany,2005.[14] P. Kühl, A. Milewska, M. Diehl, E. Molga, and H.G. Bock. NMPC for runawaysafefed-batch reactors. In Proc. Int. Workshop on Assessment and Future Directionsof NMPC, pages 467–474, 2005.[15] D.B. Leineweber. Efficient reduced SQP methods for the optimization of chemicalprocesses described by large sparse DAE models, volume 613 of Fortschr.-Ber. VDIReihe 3, Verfahrenstechnik. VDI Verlag, Düsseldorf, 1999.[16] D.Q. Mayne. Nonlinear model predictive control: Challenges and opportunities.In F. Allgöwer and A. Zheng, editors, Nonlinear Predictive Control, volume26ofProgress in Systems Theory, pages 23–44, Basel, 2000. Birkhäuser.[17] S.J. Qin and T.A. Badgwell. Review of nonlinear model predictive control applications.In B. Kouvaritakis and M. Cannon, editors, Nonlinear model predictivecontrol: theory and application, pages 3–32, London, 2001. The Institute of ElectricalEngineers.[18] V.H. Schulz, H.G. Bock, and M.C. Steinbach. Exploiting invariants in the numericalsolution of multipoint boundary value problems for DAEs. SIAM J. Sci.Comp., 19:440–467, 1998.
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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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Page 133 and 134: 124 F.A.C.C. Fontes, L. Magni, and
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Page 169 and 170: Numerical Methods for Efficient and
Page 173 and 174: L i (s i ,u i ):=Numerical Methods
Page 183: Numerical Methods for Efficient and
Page 187 and 188: 182 A. Grancharova, T.A. Johansen,
Page 199 and 200: 194 V. Sakizlis et al.presented her
Page 201 and 202: 196 V. Sakizlis et al.linear PWA fu
Page 203 and 204: 198 V. Sakizlis et al.non-linear sy
Page 205 and 206: 200 V. Sakizlis et al.Remark 1. For
Page 207 and 208: 202 V. Sakizlis et al.One should no
Page 209 and 210: 204 V. Sakizlis et al.g0.050.10.15x
Page 211 and 212: Interior-Point Algorithms for Nonli
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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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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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