Assessment and Future Directions of Nonlinear Model Predictive ...

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228 C.E. Long and E.P. Gatzke[6] ILOG. ILOG CPLEX 9.1: User’s Manual. Mountain View, CA, April 2005.[7] E. C. Kerrigan, A. Bemporad, D. Mignone, M. Morari, and J. M. Maciejowski.Multi-objective Prioritisation and Reconfiguration for the Control of ConstrainedHybrid Systems. In Proceedings of the American Controls Conference, Chicago,Illinois, 2000.[8] C. E. Long and E. P. Gatzke. Globally Optimal Nonlinear Model Predictive Control.In DYCOPS 7, the 7th International Symposium on Dynamics and Controlof Process Systems, Boston, MA, 2004.[9] C. E. Long and E. P. Gatzke. Model Predictive Control Algorithm for PrioritizedObjective Inferential Control of Unmeasured States Using Propositional Logic.Ind. Eng. Chem. Res., 44(10):3575–3584, 2005.[10] C. E. Long, P. K. Polisetty, and E. P. Gatzke. Nonlinear Model Predictive ControlUsing Deterministic Global Optimization. J. Proc. Cont., accepted, 2005.[11] G. P. McCormick. Computability of Global Solutions to Factorable NonconvexPrograms: Part I - Convex Underestimating Problems. Mathematical Programming,10:147–175, 1976.[12] R. E. Moore. Methods and Applications of Interval Analysis. SIAM, Philadelphia,1979.[13] R. S. Parker, E. P. Gatzke, and F. J. Doyle III. Advanced Model PredictiveControl (MPC) for Type I Diabetic Patient Blood Glucose Control. In Proc.American Control Conf., Chicago, IL, 2000.[14] A. N. Reyes, C. S. Dutra, and C. B. Alba. Comparison of Different PredictiveControllers with Multi-objective Optimization: Application to An Olive Oil Mill.In Proceedings of the 2002 IEEE International Conference on Control Applicationsand International Symposium on Computer Aided Control Systems Designs, pages1242–1247, Glasgow, UK, 2002.[15] H. S. Ryoo and N. V. Sahinidis. Global Optimization of Nonconvex NLPS andMINLPs with Application to Process Design. Comput. Chem. Eng., 19(5):551–566, 1995.[16] E. M. B. Smith. On the Optimal Design of Continuous Processes. PhD thesis,Imperial College, London, 1996.[17] M. L. Tyler and M. Morari. Propositional Logic in Control and Monitoring Problems.Automatica, 35:565–582, 1999.[18] J. Vada, O. Slupphaug, and T. A. Johansen. Optimal Prioritized InfeasibilityHandling in Model Predictive Control: Parameteric Preemptive MultiobjectiveLinear Programming Approach. Journal of Optimization Theory and Applications,109(2):385–413, 2001.[19] J. Vada, O. Slupphaug, T. A. Johansen., and B. A. Foss. Linear MPC withOptimal Prioritized Infeasibility Handling: Application, Computational Issues,and Stability. Automatica, 37:1835–1843, 2001.
A Nonlinear Model Predictive ControlFramework as Free Software: Outlook andProgress ReportAndrey Romanenko and Lino O. SantosGEPSI-PSE Group, Dep. de Engenharia Química, Universidade de Coimbra,Pinhal de Marrocos, Pólo II, 3030-290 Coimbra, Portugalandrew,lino@eq.uc.ptSummary. Model predictive control (MPC) has been a field with considerable researchefforts and significant improvements in the algorithms. This has led to a fairlylarge number of successful industrial applications. However, many small and mediumenterprises have not embraced MPC, even though their processes may potentially benefitfrom this control technology. We tackle one aspect of this issue with the developmentof a nonlinear model predictive control package newcon that will be released as freesoftware. The work details the conceptual design, the control problem formulation andthe implementation aspects of the code. A possible application is illustrated with anexample of the level and reactor temperature control of a simulated CSTR. Finally,the article outlines future development directions of the newcon package.1 IntroductionModel predictive control has been a field with considerable research efforts andsignificant improvements in the algorithms [3]. Also, the number of commercialMPC offerings on the market has increased in the last years. Clearly, there area number of industrial areas where MPC use is prominent because of the greateconomical benefit of this advanced control technology: refineries, petrochemical,and chemical [20].It should be noted, however, that the scope of companies that use MPC solutionsis rather limited. In order to implement a successful solution, not only is itnecessary to be able to make a very significant investment in expensive proprietaryproducts on the market, but also it is important to have in-house technicaland engineering staff able to apply and maintain them and the management torealize the benefits [7]. Because of these two factors, small and medium enterprises(SME), that play a very important role in some economies, may not knowabout the existence of MPC or may not realize the potential or, finally, may notafford it.Lately, the free and open-source software (OSS) development paradigm hasbeen gaining wide acceptance. It is mainly characterized by the rights to use,make modifications, and redistribute software subject to certain limitations.Some free software packages enjoy a big user community resulting in a fastdevelopment pace.R. Findeisen et al. (Eds.): Assessment and Future Directions, LNCIS 358, pp. 229–238, 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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NMPC for Complex Stochastic Systems
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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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