Assessment and Future Directions of Nonlinear Model Predictive ...

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Experiences with Nonlinear MPC in Polymer Manufacturing 397The engineering interface is much richer, allowing all the operator interactions,as well as allowing changes to controller tuning. Both operator and engineeringinterfaces are required to have role-based security to avoid unauthorized personnelmaking changes that affect the plant.9 BenefitsDemonstrated project results show:1. Capacity Increase: 2 − 10%2. Off-spec reduction during transition: 25 − 50%3. Off-spec reduction during steady state operation: 50 − 100%The direct quantitative benefits are significant and are typically in the region of$400, 000 to $1, 000, 000 per line per year. Qualitative benefits include:1. Minimizing product transition times2. Minimizing variability in quality3. Maximizing production capacity4. Reducing raw material consumptions5. Reducing downtime and maintenance cost6. Reducing safety stocks and slow-moving inventory10 ConclusionsThis paper has examined some of the technological and practical issues faced inimplementing nonlinear control on industrial continuous polymer manufacturingprocesses. Descriptions of modelling technology and controller technology havebeen given with emphasis on practical solutions. The importance of suitablemodels has been emphasized, and it is surely a fruitful area of research for theacademic community to find more and better ways to impose process and fundamentalknowledge onto simple control-suitable models structures – to bridgethe gap between complex continuous-time rigorous models and simple discretetimemodels with well-understood gain characteristics. In addition, it would bevery useful to develop control theoretic results on stability, observability, controllability,problem convexity, etc. within the context of these reduced scopemodels.References[1] Eric C. Kerrigan and Jon M. Maciejowski, “Designing model predictive controllerswith prioritised constraints and objectives.”, Proceeding of the IEEE Conferenceon CACSD, Glasgow, pages 309-315, (2002).[2] W.C. Li and L.T. Biegler, “Multistep, Newton-type control strategies for constrainednonlinear processes.”, Chemical Engineering Research Design, pages 562-577, (1989).
398 K. Naidoo et al.[3] N.M.C. Oliveira and L.T. Biegler, “Constraint handling and stability properties ofmodel-predictive control.”, AIChe Journal, pages 1138-1155, (1994).[4] N.M.C. Oliveira and L.T. Biegler, “An extension of Newton-type algorithms fornonlinear process control”, Automatica, pages 281-286 (1995).[5] S. Piche., B. Sayyar-Rodsari, D. Johnson, and M. Gerules, “ Nonlinear modelpredictive control using neural networks”, IEEE Control Systems Magazine, pages53-62, (2000).[6] P. Turner, J. Guiver, “Introducing the bounded derivative network - supercedingthe application of neural networks in control”, Journal of Process Control, pages407-415 (1997).[7] H. Zhao, J. Guiver , R. Neelakantan and L. Biegler, “Nonlinear Industrial ModelPredictive Controller Using Integrated PLS and Neural Net State Space Model”,IFAC World Congress, Beijing, China, (1999).[8] S.J. Wright, “Primal-Dual Interior-Point Methods.”, SIAM, (1997).
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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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450 K.R. Muske, A.E. Witmer, and R.
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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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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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