Assessment and Future Directions of Nonlinear Model Predictive ...

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ContentsFoundations and History of NMPCNonlinear Model Predictive Control: An Introductory ReviewEduardo F. Camacho, Carlos Bordons ............................... 1Theoretical Aspects of NMPCHybrid MPC: Open-Minded but Not Easily SwayedS. Emre Tuna, Ricardo G. Sanfelice, Michael J. Messina,Andrew R. Teel ................................................... 17Conditions for MPC Based Stabilization of Sampled-DataNonlinear Systems Via Discrete-Time ApproximationsÉva Gyurkovics, Ahmed M. Elaiw ................................... 35A Computationally Efficient Scheduled Model PredictiveControl Algorithm for Control of a Class of ConstrainedNonlinear SystemsMayuresh V. Kothare, Zhaoyang Wan ............................... 49The Potential of Interpolation for Simplifying PredictiveControl and Application to LPV SystemsJohn Anthony Rossiter, Bert Pluymers, Bart De Moor ................. 63Techniques for Uniting Lyapunov-Based and Model PredictiveControlPrashant Mhaskar, Nael H. El-Farra, Panagiotis D. Christofides ........ 77Discrete-Time Non-smooth Nonlinear MPC: Stability andRobustnessM. Lazar, W.P.M.H. Heemels, A. Bemporad, S. Weiland .............. 93
Page 2 and 3: Lecture Notesin Control and Informa
Page 4 and 5: Series Advisory BoardF. Allgöwer,
Page 8 and 9: VIIIContentsModel Predictive Contro
Page 10 and 11: XContentsIndustrial Perspective on
Page 12 and 13: XIIContentsRobust Model Predictive
Page 14 and 15: 2 E.F. Camacho and C. Bordonsin the
Page 16 and 17: 4 E.F. Camacho and C. Bordonswhich
Page 18 and 19: 6 E.F. Camacho and C. Bordons2.3 St
Page 20 and 21: 8 E.F. Camacho and C. Bordonsat eve
Page 22 and 23: 10 E.F. Camacho and C. Bordonscases
Page 24 and 25: 12 E.F. Camacho and C. Bordonsthe o
Page 26 and 27: 14 E.F. Camacho and C. Bordons[6] H
Page 28 and 29: 16 E.F. Camacho and C. Bordons[44]
Page 30 and 31: 18 S.E. Tuna et al.1.2 What Do We M
Page 32: 20 S.E. Tuna et al.periods while at
Page 35 and 36: Hybrid MPC: Open-Minded but Not Eas
Page 47 and 48: Conditions for MPC Based Stabilizat
Page 54 and 55: 42 É. Gyurkovics and A.M. ElaiwThe
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44 É. Gyurkovics and A.M. ElaiwRem
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46 É. Gyurkovics and A.M. Elaiw[17
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48 É. Gyurkovics and A.M. ElaiwV A
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50 M.V. Kothare and Z. Wanimproved
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52 M.V. Kothare and Z. WanAlgorithm
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54 M.V. Kothare and Z. WanAlgorithm
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56 M.V. Kothare and Z. Wan2. Given
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58 M.V. Kothare and Z. Wan120100#0#
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60 M.V. Kothare and Z. Wanwe were a
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62 M.V. Kothare and Z. Wanmodel e(k
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64 J.A. Rossiter, B. Pluymers, and
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78 P. Mhaskar, N.H. El-Farra, and P
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Discrete-Time Non-smooth Nonlinear
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106 L. Grüne, D. Nešić, and J. P
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Sampled-Data Model Predictive Contr
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Sampled-Data MPC for Nonlinear Time
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132 T. Alamo et al.In this paper we
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134 T. Alamo et al.(iii) The outer
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136 T. Alamo et al.1+f ⊤ λ1, ∀
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138 T. Alamo et al.1515X 1X 2X 1X 2
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Nonlinear Predictive Control of Irr
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152 T. Raff, C. Ebenbauer, and F. A
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164 H.G. Bock et al.real-time itera
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166 H.G. Bock et al.in a straightfo
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168 H.G. Bock et al.Structure of th
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170 H.G. Bock et al.change anymore,
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172 H.G. Bock et al.contractivity o
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174 H.G. Bock et al.to yield not on
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176 H.G. Bock et al.1.4CPU Time for
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178 H.G. Bock et al.iteration schem
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Computational Aspects of Approximat
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J(U, x(t)) =Computational Aspects o
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5 ConclusionsComputational Aspects
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Towards the Design of Parametric Mo
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208 A.G. Wills and W.P. Heatharise
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210 A.G. Wills and W.P. Heathto the
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212 A.G. Wills and W.P. Heathwhere
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214 A.G. Wills and W.P. HeathChoose
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216 A.G. Wills and W.P. Heath[11] N
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218 C.E. Long and E.P. Gatzkeformul
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220 C.E. Long and E.P. Gatzkedefine
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222 C.E. Long and E.P. GatzkeFormul
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224 C.E. Long and E.P. GatzkeVd3CV2
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226 C.E. Long and E.P. GatzkeP 1(ps
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228 C.E. Long and E.P. Gatzke[6] IL
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230 A. Romanenko and L.O. SantosThe
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232 A. Romanenko and L.O. Santospre
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234 A. Romanenko and L.O. Santosdia
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236 A. Romanenko and L.O. SantosLev
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238 A. Romanenko and L.O. Santos[11
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240 L. Magni and R. ScattoliniMPC c
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242 L. Magni and R. Scattoliniu =
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244 L. Magni and R. ScattoliniDefin
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246 L. Magni and R. Scattoliniof ti
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248 L. Magni and R. ScattoliniLetti
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250 L. Magni and R. ScattoliniThen,
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252 L. Magni and R. ScattoliniRemar
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254 L. Magni and R. Scattolini[33]
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256 M. Cannon, P. Couchmann, and B.
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270 J.M. Maciejowski, A. Lecchini V
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On Disturbance Attenuation of Nonli
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Chance Constrained Nonlinear Model
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Close-Loop Stochastic Dynamic Optim
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318 D. Limon et al.The system is su
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320 D. Limon et al.Definition 1 (Se
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322 D. Limon et al.problem may be l
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324 D. Limon et al.5.2 Robust Outpu
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[5] Bravo, J.M. and and Alamo, T. a
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328 R. Gabasov, F.M. Kirillova, and
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336 L. Blankthis is not any longer
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338 L. BlankThis is a non trivial t
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340 L. BlankThis definition is in a
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342 L. Blank(G T λ)(0) = D(x(0)
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344 L. BlankTheorem 3. S is a linea
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346 L. BlankReferences[1] Allgöwer
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348 A. Alessandri, M. Baglietto, an
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360 J.B. Jørgensen et al.In this c
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362 J.B. Jørgensen et al.with init
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364 J.B. Jørgensen et al.using the
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366 J.B. Jørgensen et al.applies (
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368 R.D. BartusiakDuring the past d
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370 R.D. BartusiakSolution Method,
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372 R.D. Bartusiaky are the model p
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374 R.D. BartusiakFig. 3. Closed lo
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376 R.D. Bartusiak• Can transfer
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378 R.D. Bartusiakfinite horizon NL
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380 R.D. BartusiakOperators and pro
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Experiences with Nonlinear MPC in P
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Integration of Advanced Model Based
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Putting Nonlinear Model Predictive
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4 State EstimationPutting Nonlinear
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420 J.V. Kadam and W. Marquardt54co
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422 J.V. Kadam and W. Marquardteffi
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424 J.V. Kadam and W. Marquardtopen
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426 J.V. Kadam and W. Marquardtt i
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428 J.V. Kadam and W. MarquardtReac
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430 J.V. Kadam and W. Marquardtis a
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432 J.V. Kadam and W. Marquardt1.21
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434 J.V. Kadam and W. Marquardt[11]
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436 J.M. Igreja, J.M. Lemos, and R.
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A Minimum-Time Optimal RechargingCo
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A Minimum-Time Optimal Recharging C
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456 P. Kühl et al.desirable. Some
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458 P. Kühl et al.Table 1. List of
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460 P. Kühl et al.process paramete
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462 P. Kühl et al.4.1 Min-Max NMPC
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464 P. Kühl et al.[7] Diehl M, Fin
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466 Z.K. Nagy et al.straightforward
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468 Z.K. Nagy et al.is introduced i
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470 Z.K. Nagy et al.Maximum likelih
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472 Z.K. Nagy et al.References[Die0
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474 A. Küpper and S. EngellIn rece
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476 A. Küpper and S. EngellFig. 2.
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478 A. Küpper and S. Engellwhere Q
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480 A. Küpper and S. Engelllength
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482 A. Küpper and S. Engellτ [min
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Receding-Horizon Estimation and Con
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496 D. Sarabia et al.the optimizati
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498 D. Sarabia et al.tachas, the co
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500 D. Sarabia et al.patterns shown
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502 D. Sarabia et al.of stages of v
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504 R. De Keyser and J. Donald IIIs
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506 R. De Keyser and J. Donald III
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508 R. De Keyser and J. Donald IIIu
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510 R. De Keyser and J. Donald IIIT
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512 R. De Keyser and J. Donald IIIt
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514 A. Deshpande, S.C. Patwardhan,
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A Low Dimensional Contractive NMPC
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538 D. DeHaan and M. Guayadapted on
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540 D. DeHaan and M. Guayτ ∈ [t
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542 D. DeHaan and M. Guaywithin the
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544 D. DeHaan and M. GuayThe functi
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546 D. DeHaan and M. Guay365360LQRP
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548 D. DeHaan and M. Guaythe parame
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550 D. DeHaan and M. Guayand the bo
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552 S. Gros et al.approaches have b
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554 S. Gros et al.Ṡ = −H xx + S
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556 S. Gros et al.3E 31hdGE 2GdE 1E
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558 S. Gros et al.translation from
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560 S. Gros et al.The choice of the
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562 S. Gros et al.5 ConclusionThis
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Receding Horizon Control for Free-F
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An Experimental Study of Stabilizin
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Coordination of Networked Dynamical
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592 A.N. Venkat, J.B. Rawlings, and
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Distributed MPC for Dynamic Supply
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618 S.V. Raković and D.Q. Maynewhe
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620 S.V. Raković and D.Q. Mayneass
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622 S.V. Raković and D.Q. MayneDef
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624 S.V. Raković and D.Q. Maynez 0
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626 S.V. Raković and D.Q. Mayne5 C
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Trajectory Control of Multiple Airc
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642 Author IndexGuiver, John 383Gyu
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Vol. 331: Antsaklis, P.J.; Tabuada,
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