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Integration of Advanced Model Based Control with Industrial IT 405piecewise linear. All other model variables are evaluated at the sample timepoints. This means that overall 85540 variables are present in the on-line optimizationproblem. The solution time is about five minutes for a cold start of thesolver and about 40 seconds for a subsequent solver run. Please see [8] for detailsabout the numerical formulation and solution of the optimization problem.Figure 3 shows an operator display for boiler start-up optimization. The trendplot displays the manipulated variables in the upper pane, the main processvariables (live steam parameters) in the middle pane and constrained thermalstresses in the lower pane. As a result of the optimization, the process is drivenalong the allowed limits for thermal stresses.Traditionally an operator display shows current process values and historylogs. As a by-product of model predictive control, the operator can additionallysee the prediction of the future behavior of the plant. As the NMPC runsintegrated with the control system, this display can easily be configured.Using the NMPC, the start-up time could be reduced by about 20 minutesand the start-up costs by 10% as compared to a well tuned classical control.4 ConclusionsNonlinear Model-based Predictive Control (NMPC) is a promising control technology.Due to advances in computational algorithms during recent years, it isnow possible formulate and solve the underlying large-scale nonlinear optimizationproblems on-line under real-time conditions. The example discussed herewas developed in detail in [8].For a successful application of NMPC it is equally important to appropriatelyintegrate the method with the control system. This paper discusses how thisis done with the Industrial IT System 800xA by ABB. Based on internationalstandards for control systems engineering and software, the System 800xA architectureand the Aspect Object technology allow a flexible integration of modelknowledge and model based applications. Two new aspects have been developedin the Dynamic Optimization system extension. The new aspects can be combinedwith other available aspects, e.g. for controller connectivity, history logsand process graphics.The NMPC runs on an application server that is integrated as additional nodewith the system. Installation and maintenance are identical to other nodes, likedata servers and display clients.This paper uses the start-up of a power plant as example. Batch processes areanother promising application area, as described in [11].References[1] ABB Automation Technologies. Industrial IT System 800xA – System ArchitectureOverview. http://www.abb.com, Document Id: 3BUS092080R0101, 2005.[2] Lorenz T. Biegler. Efficient solution of dynamic optimization and NMPC problems.In Frank Allgöwer and Alex Zheng, editors, Nonlinear Model PredictiveControl, pages 219–243. Birkhäuser, Basel, 2000.
406 R. Franke and J. Doppelhamer[3] L.G. Bratthall, R. van der Geest, H. Hoffmann, E. Jellum, Z. Korendo, R. Martinez,M. Orkisz, C. Zeidler, and J. S Andersson. Integrating hundred’s of productsthrough one architecture – the Industrial IT architecture. In International Converenceon Software Engineering. Orlando, Florida, USA, 2002.[4] M. Diehl, R. Findeisen, S. Schwarzkopf, I. Uslu, F. Allgöwer, H.G. Bock, and J.P.Schlöder. An efficient algorithm for optimization in nonlinear model predictivecontrol of large-scale systems. at – Automatisierungstechnik, 50(12), 2002.[5] H. Elmqvist, H. Tummescheit, and M. Otter. Modeling of thermo-fluid systems– Modelica.Media and Modelica.Fluid. In Proceedings of the 3rd InternationalModelica Conference. Modelica Association, Linköping, Sweden, November 2003.[6] R. Franke and E. Arnold. Applying new numerical algorithms to the solutionof discrete-time optimal control problems. In K. Warwick and M. Kárný, editors,Computer-Intensive Methods in Control and Signal Processing: The Curse ofDimensionality, pages 105–118. Birkhäuser Verlag, Basel, 1997.[7] R. Franke, E. Arnold, and H. Linke. HQP: a solver for nonlinearly constrainedlarge-scale optimization. http://hqp.sourceforge.net.[8] R. Franke, K. Krüger, and M. Rode. Nonlinear model predictive control for optimizedstartup of steam boilers. In GMA-Kongress 2003. VDI-Verlag, Düsseldorf,2003. VDI-Berichte Nr. 1756, ISBN 3-18-091756-3.[9] International Electrotechnical Commission. Industrial systems, installations andequipment and industrial products – structuring principles and reference designations.IEC Standard 61346, 1996.[10] Modelica Association. Modelica – A Unified Object-Oriented Language for PhysicalSystems Modeling, Version 2.2. http://www.modelica.org, 2005.[11] Z.K. Nagy, B. Mahn, R. Franke, and F. Allgöwer. Nonlinear model predictivecontrol of batch processes: an industrial case study. In 16th IFAC World Congress.Prague, Czech Republic, July 2005.[12] M. Otter, J. Årzén, and A. Schneider. StateGraph – a Modelica library for hierarchicalstate machines. In Proceedings of the 4th International Modelica Conference.Modelica Association, Hamburg-Harburg, Germany, March 2005.
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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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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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