Assessment and Future Directions of Nonlinear Model Predictive ...

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230 A. Romanenko and L.O. SantosThe advantages of such development model for academia and research areevident, especially for computing intensive fields [25]. Octave [6], R [12], andASCEND [19] are examples of successful free software projects.Besides, new “open source” business models for commercial companies havebeen outlined [10, 14]: distribution and technical support, in which a set ofOSS packages are customized and tuned to a specific platform; education, withOSS used as pedagogical material; hardware sale, in which OSS adds value tothe hardware being marketed; custom development, in which OSS is tailoredfor the needs of a particular user; proprietary-open source mixture, in which aproprietary enhancement to an open source program is available for a fee.This type of software has a great potential in the field of process control,especially if some of its disadvantages are overcome [21].As an alternative to proprietary software for nonlinear model predictive control(NMPC) and as a way to enable SMEs to use NMPC, we have been developingnewcon, a solution based on open-source and free software. The detailsof the underlying technology may be found elsewhere [23]. For the core elementsof the NMPC framework, the ODE solver with sensitivity analysis capabilitiesand an optimizer, we use highly efficient third party libraries developed as opensourceand free software [9, 11]. Once ready for an initial release, this NMPCframework will be available for educational institutions for teaching and researchentities for testing and further improvement. There is a technological spin-off inthe process of creation whose role is to promote the development of the packageand to deploy MPC application in the free software paradigm. Besides, the codewill be available for download for anybody, subject to a free software license.Other related software packages in the field of NMPC include the OctaveNMPC package [26], Omuses [8], a robust NMPC package [15], the optimalcontrol package MUSCOD-II [4], and the automatic code generation system fornonlinear receding horizon control AutoGenU [16].A description of the implemented control formulation along with a designoverview of newcon is given in Section 2. Also, information on the ODE andoptimizer solvers is provided in Section 2.2. The application of newcon is illustratedwith a nonlinear example in Section 3. Finally, some remarks and futuredirections are pointed out in Section 4.2 Nonlinear MPC FrameworkThe nonlinear MPC framework newcon proposed here is based on the Fortranpackage developed by [23]. It implements a control formulation with a multipleshooting strategy to perform the NMPC predictions as described in [22], and isbased on the Newton-type control formulation approach established by [17, 18].One of the main concerns in the design and the implementation phases ofnewcon is to make it modular so that existing components and software, such asa regulatory control and automation layers, could be integrated in the resultingadvanced control system. The whole point of deploying newcon in small and
A Nonlinear Model Predictive Control Framework as Free Software 231Fig. 1. newcon Component diagrammedium companies is not to replace existing systems and components, but ratherto build upon them.The conceptual design of newcon (Figure 1) incorporates several moduleswith distinct features.The Controller block contains code necessary for the control problem formulation(as described in Section 2.1) and interface routines to an ODE and a QPsolvers. Besides, in order to reduce the communication overhead, the systemmodel also makes part of this module. Moreover, an Estimator component maybe used to update the model states and parameters, hence reducing model-plantmismatch.The purpose of the Data exchange and synchronization component is to providethe means for consistent dataflows and to ensure correct timing in theoverall system. This is achieved by the use of POSIX shared memory and synchronizationmechanisms. Alternatively, when the timing requirements are notstrict, this module may be based on a database engine.The function of the Communications block is to interface newcon to theexisting regulatory control system of the plant using open industrial communicationprotocols. Because of the widespread use and low cost of the hardware,open TCP/IP based protocols running over Ethernet hardware will be favored.This module may implement capabilities/protocols necessary in a particular application,adding to the flexibility of the overall system and reducing its size.The measurements of the plant, the controller states, setpoints, outputs, performanceindices, as well as other relevant information is recorded by the loggermodule. This module currently supports data saving in plain format in severaltext datafiles. Its functionality will be expanded to include database support.The graphical user interface (GUI) module provides a user friendly way tocontrol the system, to monitor graphically important variable trends and performanceindicators. In order to distribute computing resources evenly, and to
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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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Page 183 and 184: Numerical Methods for Efficient and
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Page 187 and 188: 182 A. Grancharova, T.A. Johansen,
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Page 201 and 202: 196 V. Sakizlis et al.linear PWA fu
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Page 243 and 244: Robustness and Robust Design of MPC
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Page 263 and 264: MPC for Stochastic Systems 259we ha
Page 265 and 266: MPC for Stochastic Systems 261form
Page 267 and 268: MPC for Stochastic Systems 263( )κ
Page 269 and 270: MPC for Stochastic Systems 265Fig.
Page 271 and 272: MPC for Stochastic Systems 267⎡
Page 273 and 274: 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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