Assessment and Future Directions of Nonlinear Model Predictive ...

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14 E.F. Camacho and C. Bordons[6] H.G. Bock, M.M. Diehl, D.B. Leineweber, and J.P. Schloder. Nonlinear ModelPredictive Control, chapter a Direct Multiple Shooting Method for Real-time Optimizationof Nonlinear DAE Processes. Birkhäuser, 2000.[7] E.F. Camacho, M. Berenguel, and F.R. Rubio. Advanced Control of Solar PowerPlants. Springer-Verlag, London, 1997.[8] H. Chen and F. Allgöwer. A Quasi-infinite Horizon Nonlinear Predictive ControlScheme with Guaranteed Stability. Automatica, 34(10):1205–1218, 1998.[9] D.W. Clarke, C. Mohtadi, and P.S. Tuffs. Generalized Predictive Control. Part I.The Basic Algorithm. Automatica, 23(2):137–148, 1987.[10] C.R. Cutler and B.C. Ramaker. Dynamic Matrix Control- A Computer ControlAlgorithm. In Automatic Control Conference, San Francisco, 1980.[11] F.J. Doyle, R.K. Pearson, and B.A. Ogunnaike. Identification and Control UsingVolterra Models. Springer, 2001.[12] R. Findeisen and F. Allgower. An Introduction to Nonlinear Model PredictiveControl. In 21st Benelux Meeting on Systems and Control, Veldhoven, 2002.[13] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. State and Output NonlinearModel Predictive Control: An Overview. European Journal of Control, 9:190–206,2003.[14] K. Hornik, M. Stinchombe, and H. White. Multilayer Feedforward Networks areUniversal Approximators. Neural networks, pages 359–366, 1989.[15] Shi-Shang Jang, Babu Joseph, and Hiro Mukai. Comparison of two approachesto on-line parameter and state estimation of nonlinear systems. Industrial andEngineering Chemistry Process Design and Development, 25:809–814, 1986.[16] Y. Jin, X. Sun, and C. Fang. Adaptive Control of Bilinear Systems with BoundedDisturbances, and its Application. Control Engineering Practice, pages 815–822,1996.[17] T.A. Johansen, J.T. Evans, and B.A. Foss. Identification of Nonlinear SystemStructure and Parameters Using Regime Decomposition. Automatica, pages 321–326, 1995.[18] S.S. Keerthi and E.G. Gilbert. Optimal Infinite-horizon Feedback Laws for a GeneralClass of Constrained Discrete-time Systems: Stability and Moving-horizonApproximations. J. Optim. Theory Appl., 57(2):265–293, 1988.[19] R.M.C. De Keyser. A Gentle Introduction to Model Based Predictive Control. InPADI2 International Conference on Control Engineering and Signal Processing,Piura, Peru, 1998.[20] R.M.C. De Keyser and A.R. Van Cuawenberghe. Extended Prediction SelfadaptiveControl. In IFAC Symposium on Identification and System ParameterEstimation, York,UK, pages 1317–1322, 1985.[21] B. Kouvaritakis, M. Cannon, and J.A. Rossiter. Stability, Feasibility, Optimalityand the Number of Degrees of Freedom in Constrained Predictive Control. InSymposium on Non-linear Predictive Control. Ascona, Switzerland, 1998.[22] W. H. Kwon, A. M. Bruckstein, and T. Kailath. Stabilizing state-feedback designvia the moving horizon method. International Journal of Control, 37(3):631–643,1983.[23] W. S. Levine. The Control Handbook, chapter Volterra and Fliess Series Expansionsfor Nonlinear Systems. CRC/IEEE Press, 1996.[24] L. Magni, G. De Nicolao, and R. Scattolini. Output feedback and tracking ofnonlinear systems with model predictive control. Automatica, 37:1601–1607, 2001.
Nonlinear Model Predictive Control: An Introductory Review 15[25] B.R. Maner, F. J. Doyle, B.A. Ogunnaike, and R.K. Pearson. Nonlinear ModelPredictive Control of a Multivariable Polymerization Reactor unsing Second OrderVolterra Models. Automatica, 32:1285–1302, 1996.[26] D.Q. Mayne. Nonlinear model predictive control: An assessment. In Fifth InternationalConference on Chemical Process Control - CPC V, pages 217–231,1996.[27] D.Q. Mayne, J.B. Rawlings, C.V. Rao, and P.O.M. Scokaert. Constrained ModelPredictive Control: Stability and Optimality. Automatica, 36:789–814, 2000.[28] H. Michalska and D.Q. Mayne. Robust receding horizon control of constrainednonlinear systems. IEEE Trans. on Automatic Control, 38(11):1623–1633, 1993.[29] H. Michalska and D.Q. Mayne. Moving horizon observers and observer-basedcontrol. IEEE Trans. on Automatic Control, 40(6):995–1006, 1995.[30] K.R. Muske and J. Rawlings. Model Predictive Control with Linear Models.AIChE Journal, 39:262–287, 1993.[31] G. De Nicolao, L. Magni, and R. Scattolini. Nonlinear Model Predictive Control,chapter Nonlinear Receding Horizon Control of Internal Combustion Engines.Birkhäuser, 2000.[32] M. Norgaard, O. Ravn, N.K. Poulsen, and L.K. Hansen. Neural Networks forModelling and Control of Dynamic Systems. Springer, London, 2000.[33] S. Norquay, A. Palazoglu, and J.A. Romagnoli. Application of Wiener Model PredictiveControl (WMPC) to a pH Neutralization Experiment. IEEE Transactionson Control Systems Technology, 7(4):437–445, 1999.[34] Miguel Peña,E.F.Camacho,S.Piñón, and R. Carelli. Model Predictive Controllerfor Piecewise Affine System. In Proceedings of the 16th IFAC WorldCongress, Prague, Czech Rebublic, 2005.[35] S.J. Qin and T.A. Badgwell. An Overview of Industrial Model Predictive ControlTechnology. In Chemical Process Control: Assessment and New Directions forResearch. In AIChE Symposium Series 316, 93. Jeffrey C. Kantor, Carlos E.Garcia and Brice Carnahan Eds. 232-256, 1997.[36] S.J. Qin and T.A. Badgwell. An Overview of Nonlinear Model Predictive ControlApplications. In IFAC Workshop on Nonlinear Model Predictive Control.Assessment and Future Directions. Ascona (Switzerland), 1998.[37] Christopher V. Rao, James B. Rawlings, and Jay H. Lee. Constrained linear stateestimation. A moving horizon approach. Automatica, 37(10):1619–1628, 2001.[38] Christopher V. Rao, James B. Rawlings, and David Q. Mayne. Constrained stateestimation for nonlinear discrete-time systems: Stability and moving horizon approximations.IEEE Transactions on Automatic Control, 2000.[39] J. Richalet, A. Rault, J.L. Testud, and J. Papon. Algorithmic Control of IndustrialProcesses. In 4 th ifac Symposium on Identification and System ParameterEstimation. Tbilisi ussr, 1976.[40] J. Richalet, A. Rault, J.L. Testud, and J. Papon. Model Predictive HeuristicControl: Application to Industrial Processes. Automatica, 14(2):413–428, 1978.[41] D.G. Robertson and J.H. Lee. A least squares formulation for state estimation.J. Proc. Contr., 5(4):291–299, 1995.[42] Louis P. Russo and Robert E. Young. Moving horizon state estimation appliedto an industrial polymerization process. In Proceedings of 1999 American ControlConference, San Diego, California, 1999.[43] P.O.M. Scokaert, D.Q. Mayne, and J.B. Rawlings. Suboptimal model predictivecontrol (feasibility implies stability). IEEE Transactions on Automatic Control,44(3):648–654, 1999.
Page 2 and 3: Lecture Notesin Control and Informa
Page 4 and 5: Series Advisory BoardF. Allgöwer,
Page 7 and 8: ContentsFoundations and History of
Page 9 and 10: ContentsIXRobustness, Robust Design
Page 11 and 12: ContentsXIHybrid NMPC Control of a
Page 13 and 14: Nonlinear Model Predictive Control:
Page 25: Nonlinear Model Predictive Control:
Page 29 and 30: Hybrid MPC: Open-Minded but Not Eas
Page 31 and 32: Hybrid MPC: Open-Minded but Not Eas
Page 34 and 35: 22 S.E. Tuna et al.this says that i
Page 36 and 37: 24 S.E. Tuna et al.In particular, f
Page 38 and 39: 26 S.E. Tuna et al.W N (f(x, ¯κ N
Page 40 and 41: 28 S.E. Tuna et al.where ᾱ := max
Page 42 and 43: 30 S.E. Tuna et al.21.521.5µ=51
Page 44 and 45: 32 S.E. Tuna et al.obstacle and gra
Page 46 and 47: 34 S.E. Tuna et al.[19] C. Prieur.
Page 48 and 49: 36 É. Gyurkovics and A.M. Elaiw[8]
Page 50 and 51: 38 É. Gyurkovics and A.M. ElaiwWe
Page 52: 40 É. Gyurkovics and A.M. ElaiwWe
Page 55 and 56: Conditions for MPC Based Stabilizat
Page 61 and 62: A Computationally Efficient Schedul
Page 75 and 76: 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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Nonlinear Model Predictive Control:
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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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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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