Assessment and Future Directions of Nonlinear Model Predictive ...

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Integration of Advanced Model Based Controlwith Industrial ITRüdiger Franke 1 and Jens Doppelhamer 21 ABB AG, Power Generation, Mannheim, GermanyRuediger.Franke@de.abb.com2 ABB Corporate Research, Ladenburg, GermanyJens.Doppelhamer@de.abb.comSummary. Advanced model based control is a promising technology that can improvethe productivity of industrial processes. In order to find its way into regular applications,advanced control must be integrated with the industrial control systems. Moderncontrol systems, on the other hand, need to extend the reach of traditional automationsystems – beyond control of the process – to also cover the increasing amount ofinformation technology (IT) required to successfully operate industrial processes in today’sbusiness markets. The Industrial IT System 800xA from ABB provides a scalablesolution that spans and integrates loop, unit, area, plant, and interplant controls.This paper introduces the 800xA and the underlying Aspect Object technology.It is shown how model knowledge and optimization solver technology are integratedinto the 800xA framework. This way, advanced model based control solutions canbesetupinanopenandmodularlystructuredway.Newmodelandsolveraspectscan be combined with available aspects covering standard functionality like processconnectivity, management of process data, trend&history data and application data,as well as operator graphics.A Nonlinear Model-based Predictive Controller (NMPC) for power plant start-up istreated as example. This paper discusses how NMPC can be integrated with a moderncontrol system so that standard concepts are re-used for this advanced model basedcontrol concept.1 IntroductionDuring the last decades, several advanced control technologies have been developed,including adaptive control, fuzzy control and neuro control. While eachof these technologies offers advantages over classical control methods, PID controllersstill dominate the vast majority of industrial applications.One reason for the lack of mainstream use of advanced control technologies isseen in the fact that they require specialized engineering knowledge and tools.Normally, specialized experts are required to apply advanced control methods. Abetter integration of advanced control technologies with regular control systemsis seen as a key factor for improved acceptance.Nonlinear model based control (NMPC) has received much attention duringthe last years. The technology has several advantages from a control point ofview: it accommodates nonlinear, multi-variable problems with state constraints.R. Findeisen et al. (Eds.): Assessment and Future Directions, LNCIS 358, pp. 399–406, 2007.springerlink.com c○ Springer-Verlag Berlin Heidelberg 2007
400 R. Franke and J. DoppelhamerImportant achievements have been made to treat the computationally challengingtask of formulating and solving large-scale nonlinear optimization problemson-line [2, 4, 6]. Moreover, NMPC has the advantage that the technology is moreopen, compared to other advanced control methods. Models do represent the behaviorof a plant and standard optimization algorithms are used to apply themodels to control. This openness improves the acceptance of NMPC on the onehand side.On the other side, still special purpose tools are required to implement modelbased control. This implies that concepts which are readily available in a standardcontrol system need to be specifically interfaced or even redeveloped forapplications of model based control, including e.g. signal exchange with sensors,actuators and low level controls, operator graphics, trend&history display, signalingof alarms and events, as well as system maintenance. This is seen as animportant burden for both: acceptance and cost of NMPC.2 The Industrial IT System 800xA2.1 System OverviewThe Industrial IT System 800xA seamlessly integrates traditionally isolatedplant devices and systems, extending the reach of the automation system toall plant areas. The result is a simplified, software representation of the plant,from simple on/off-type switches and valves to smart field devices, dedicatedcontrol subsystems, and PC-based supervisory systems [1].The framework for the 800xA system architecture is built upon ABB’s AspectObject technology. Aspect Objects relate plant data and functions – the aspects,to specific plant assets – the objects. Aspect objects represent real objects, suchas process units, devices and controllers. Aspects are informational items, suchas I/O definitions, engineering drawings, process graphics, reports and trendsthat are assigned to the objects in the system.Aspect Objects are organized in hierarchical structures that represent differentviews of the plant. One object may be placed multiple times in differentstructures. Examples for different types of structures are:Functional Structure: Shows the plant from the process point of view.Location Structure: Shows the physical layout of what equipment is locatedwhere in the plant.Control Structure: Shows the control network in terms of networks, nodes,fieldbuses, and stations.The idea of placing the same object in multiple structures is based on the IECstandard 1346 [3, 9].The Plant Explorer is the main tool used to create, delete, and organize AspectObjects and aspects. It is based on a structural hierarchy, similar to WindowsExplorer, as demonstrated in Figure 1. The object hierarchy is visible on the lefthand side of the window. The upper right pane shows the aspects of an objectand the lower right pane views a selected aspect.
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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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452 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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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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