Literatura académica sobre el tema "Networked Model Predictive Control"
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Artículos de revistas sobre el tema "Networked Model Predictive Control"
Wu, Jing, Liqian Zhang y Tongwen Chen. "Model predictive control for networked control systems". International Journal of Robust and Nonlinear Control 19, n.º 9 (junio de 2009): 1016–35. http://dx.doi.org/10.1002/rnc.1361.
Texto completoOnat, Ahmet, A. Teoman Naskali y Emrah Parlakay. "Model Based Predictive Networked Control Systems". IFAC Proceedings Volumes 41, n.º 2 (2008): 13000–13005. http://dx.doi.org/10.3182/20080706-5-kr-1001.02198.
Texto completoVaccarini, Massimo, Sauro Longhi y M. Reza Katebi. "Unconstrained networked decentralized model predictive control". Journal of Process Control 19, n.º 2 (febrero de 2009): 328–39. http://dx.doi.org/10.1016/j.jprocont.2008.03.005.
Texto completoHe, Fang, Xiao Li y Qiang Wang. "Study of Predictive Control in Industrial Networked Control System". Advanced Materials Research 201-203 (febrero de 2011): 2087–90. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2087.
Texto completoUlusoy, Alphan, Ahmet Onat y Ozgur Gurbuz. "Wireless Model Based Predictive Networked Control System". IFAC Proceedings Volumes 42, n.º 3 (2009): 40–47. http://dx.doi.org/10.3182/20090520-3-kr-3006.00007.
Texto completoEwald, Grzegorz y Mietek A. Brdys. "Model Predictive Controller for Networked Control Systems". IFAC Proceedings Volumes 43, n.º 8 (2010): 274–79. http://dx.doi.org/10.3182/20100712-3-fr-2020.00046.
Texto completoChen, Zai Ping y Xue Wang. "Research on Networked Control Systems Based on Adaptive Predictive Control". Applied Mechanics and Materials 441 (diciembre de 2013): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amm.441.833.
Texto completoZhang, Yingwei, Xue Chen y Renquan Lu. "Performance of Networked Control Systems". Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/382934.
Texto completoKamal, Faria y Badrul Chowdhury. "Model predictive control and optimization of networked microgrids". International Journal of Electrical Power & Energy Systems 138 (junio de 2022): 107804. http://dx.doi.org/10.1016/j.ijepes.2021.107804.
Texto completoBernardini, D., M. C. F. Donkers, A. Bemporad y W. P. M. H. Heemels. "A Model Predictive Control Approach for Stochastic Networked Control Systems". IFAC Proceedings Volumes 43, n.º 19 (2010): 7–12. http://dx.doi.org/10.3182/20100913-2-fr-4014.00007.
Texto completoTesis sobre el tema "Networked Model Predictive Control"
Droge, Greg Nathanael. "Behavior-based model predictive control for networked multi-agent systems". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51864.
Texto completoQiu, Quanwei. "Networked Model Predictive Control for Microgrids with Distributed PV Generators". Thesis, Griffith University, 2020. http://hdl.handle.net/10072/400460.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
Full Text
Henriksson, Erik. "Predictive Control for Wireless Networked Systems in Process Industry". Doctoral thesis, KTH, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141459.
Texto completoQC 20140217
Varutti, Paolo [Verfasser]. "Model Predictive Control for Nonlinear Networked Control Systems : A Model-based Compensation Approach for Nondeterministic Communication Networks / Paolo Varutti". Aachen : Shaker, 2014. http://d-nb.info/1053361688/34.
Texto completoFilippo, Marco. "Stabilizing nonlinear model predictive control in presence of disturbances and off - line approximations of the control law". Doctoral thesis, Università degli studi di Trieste, 2011. http://hdl.handle.net/10077/4519.
Texto completoOne of the more recent and promising approaches to control is the Receding Horizon one. Due to its intrinsic characteristics, this methodology, also know as Model Predictive Control, allows to easily face disturbances and model uncertainties: indeed at each sampling instant the control action is recalculated on the basis of the reached state (closed loop). More in detail, the procedure consists in the minimization of an adequate cost function with respect to a control input sequence; then the first element of the optimal sequence is applied. The whole procedure is then continuously reiterated. In this thesis, we will focus in particular on robust control of constrained systems. This is motivated by the fact that, in practice, every real system is subjected to uncertainties, disturbances and constraints, in particular on state and input (for instance, plants can work without being damaged only in a limited set of configurations and, on the other side, control actions must be compatible with actuators' physical limits). With regard to the first aspect, maintaining the closed loop stability even in presence of disturbances or model mismatches can result in an essential strategy: moreover it can be exploited in order to design an approximate stabilizing controller, as it will be shown. The control input values are obtained recurring to a Nearest Neighbour technique or, in more favourable cases, to a Neural Network based approach to the exact RH law, which can be then calculated off line: this implies a strong improvement related to the applicability of MPC policy in particular in terms of on line computational burden. The proposed scheme is capable to guarantee stability even for systems that are not stabilizable by means of a continuous feedback control law. Another interesting framework in which the study of the influence of uncertainties on stability can lead to significant contributions is the networked MPC one. In this case, due to the absence of physical interconnections between the controller and the systems to be controlled, stability can be obtained only taking into account of the presence of disturbances, delays and data losses: indeed this kind of uncertainties are anything but infrequent in a communication network. The analysis carried out in this thesis regards interconnected systems and leads to two distinct procedures, respectively stabilizing the linear systems with TCP protocol and nonlinear systems with non-acknowledged protocol. The core of both the schemes resides in the online solution of an adequate reduced horizon optimal control problem.
Una delle strategie di controllo emerse più recentemente, più promettenti e di conseguenza più studiate negli ultimi anni è quella basata sull'approccio Receding Horizon. Grazie alle caratteristiche che contraddistinguono questa tecnica, cui si fa spesso riferimento anche col nome di Model Predictive Control, risulta piuttosto agevole trattare eventuali disturbi e incertezze di modellazione; tale metodo prevede infatti il calcolo di un nuovo ingresso di controllo per ciascun istante di campionamento, in seguito alla minimizzazione ad ogni passo di un'opportuna funzione di costo rispetto ad una sequenza di possibili futuri ingressi, inizializzata sulla base del valore dello stato del sistema all'istante considerato. Il controllo è dato dal primo elemento di tale sequenza ottima; tutto questo viene continuamente ripetuto, il che comporta un aggiornamento costante del segnale di controllo. Gli inconvenienti di questa tecnica risiedono nelle elevate risorse computazionali e nei tempi di calcolo richiesti, così da ridurne drasticamente l'applicabilità specie nel caso di sistemi con elevata dinamica. In questa tesi ci si concentrerà sulle caratteristiche di robustezza del controllore: l'importanza di quest'analisi risiede nel fatto che ogni sistema reale è soggetto a incertezze e disturbi di varia origine cui bisogna far fronte durante le normali condizioni di funzionamento. Inoltre, la capacità di gestire errori di modellazione, come si vedrà, può essere sfruttata per ottenere un notevole incremento delle prestazioni nella stima del valore da fornire in ingresso all'impianto: si tratta di ripartire l'errore complessivo in modo da garantirsi dei margini che consentano di lavorare con un'approssimazione della legge di controllo, come specificato più avanti. In tutto il lavoro si considereranno sistemi vincolati: l'interesse per questa caratteristica dipende dal fatto che nella pratica vanno sempre tenuti in considerazione eventuali vincoli su stato e ingressi: basti pensare al fatto che ogni impianto è progettato per lavorare solo all'interno un determinato insieme di configurazioni, determinato ad esempio da vincoli fisici su attuatori, sensori e così via: non riporre sufficiente attenzione in tali restrizioni può risultare nel danneggiamento del sistema di controllo o dell'impianto stesso. Le caratteristiche di stabilità di un sistema controllato mediante MPC dipendono in modo determinante dalla scelta dei parametri e degli attributi della funzione di costo da minimizzare; nel seguito, con riferimento al caso dei sistemi non lineari, saranno forniti suggerimenti e strumenti utili in tal senso, al fine di ottenere la stabilità anche in presenza di disturbi (che si assumeranno opportunamente limitati). Successivamente tale robustezza verrà sfruttata per la progettazione di controllori stabilizzanti approssimati: si dimostrerà infatti che, una volta progettato adeguatamente il sistema di controllo “esatto” basato su approccio RH e conseguentemente calcolati off-line i valori ottimi degli ingressi su una griglia opportunamente costruita sul dominio dello stato, il ricorso a una conveniente approssimazione di tali valori non compromette le proprietà di stabilità del sistema complessivo, che continua per di più a mantenere una certa robustezza. Da notare che ciò vale anche per sistemi non stabilizzabili mediante legge di controllo feedback continua: la funzione approssimante può essere ottenuta in questo caso con tecniche di tipo Nearest Neighbour; qualora invece la legge di controllo sia sufficientemente regolare si potrà far ricorso ad approssimatori smooth, quali ad esempio le reti neurali. Tutto ciò comporta un notevole miglioramento delle prestazioni del controllore RH sia dal punto di vista del tempo di calcolo richiesto che (nel secondo caso) della memoria necessaria ad immagazzinare i parametri del controllore, risultando nell'applicabilità dell'approccio basato su MPC anche al caso di sistemi con elevata dinamica. Un altro ambito in cui lo studio dell'influenza delle incertezze e dei disturbi sulla stabilità richiede una notevole attenzione è quello dei sistemi networked; anche in questo caso il ricorso all'MPC può portare a ottimi risultati di stabilità robusta, a patto di individuare un' opportuna struttura per il sistema complessivo ed effettuare scelte adeguate per il problema di ottimizzazione. In particolare, si considererà il caso di trasmissione di dati tra un controllore centralizzato e le varie parti dell'impianto in assenza di collegamento fisico diretto. Lo studio della stabilità dovrà allora tenere in considerazione la presenza di perdite di pacchetti o ritardi di trasmissione, condizioni tutt'altro che infrequenti per le reti. Saranno quindi proposte due distinte procedure, che si dimostreranno essere in grado di garantire robustezza a sistemi rispettivamente lineari comunicanti con protocolli di tipo TCP e non lineari in presenza di protocolli UDP. Questo secondo caso è senz'altro il più complesso ma allo stesso tempo il più concreto tra i due. Il nucleo del controllo è ancora basato su una tecnica MPC, ma stavolta il controllore è chiamato a risolvere il problema di ottimizzazione su un orizzonte “ridotto”, che consente la gestione dei ritardi e di eventuali perdite di pacchetto su determinati canali. La lunghezza dell'orizzonte dipenderà dalla presenza o meno dei segnali di ricezione del pacchetto (acknowledgement).
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Alrifaee, Bassam [Verfasser], Dirk [Akademischer Betreuer] Abel y Christoph [Akademischer Betreuer] Ament. "Networked model predictive control for vehicle collision avoidance / Bassam Alrifaee ; Dirk Abel, Christoph Ament". Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1158599595/34.
Texto completoWinqvist, Rebecka. "Neural Network Approaches for Model Predictive Control". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284323.
Texto completoModell-prediktiv reglering (MPC) är en strategi inom återkopplad regleringmed rötter i optimeringsteori. MPC:n använder sig av en dynamiskmodell för att prediktera de framtida värdena på systemets styrvariabler.Den löser sedan ett optimeringsproblem för att beräkna en optimalstyrsignal som minimerar skillnaden mellan referensvärdena och depredikterade värdena. Att lösa det associerade optimeringsproblemetonline kan medföra höga beräkningskostnader, något som utgör en av dehuvudsakliga begränsningarna med traditionell MPC. Olika offline-strategierhar föreslagits för att kringgå detta, däribland explicit modell-prediktivreglering (eMPC) samt senare inlärningsmetoder baserade på neuronnät.Den här masteruppsatsen undersöker ett ramverk för träning och utvärderingav olika neuronnätsstrukturer för MPC-inlärning. En ny metod för effektivgenerering av träningsdata presenteras som en del av detta ramverk.Fyra olika nätstrukturer studeras; ett black box-nät samt tre nät sominkluderar MPC-specifik information. Näten evalueras i termer av två olikaprestandamått genom experiment på realistiska två- och fyrdimensionellasystem. Experimenten visar att en MPC-specifik nätstruktur resulterar iökad prestanda när mängden träningsdata är begränsad, men att de fyranäten presterar likvärdigt när mycket träningsdata finns att tillgå. De visarvidare att ett återkopplat neuronnät som tränas på både tillstånds- ochstyrsignalstrajektorier från en familj av MPC:er har förmågan att generaliseravid påträffandet av nya MPC-problem. De föreslagna metoderna i den häruppsatsen utgör ett första steg mot utvecklandet av ett enhetligt ramverk förkaraktärisering av inlärningsmetoder i termer av både modellvalidering ocheffektiv datagenerering.
Pin, Gilberto. "Robust nonlinear receding horizon control with constraint tightening: off line approximation and application to networked control system". Doctoral thesis, Università degli studi di Trieste, 2009. http://hdl.handle.net/10077/3122.
Texto completoNonlinear Receding Horizon (RH) control, also known as moving horizon control or nonlinear Model Predictive Control (MPC), refers to a class of algorithms that make explicit use of a nonlinear process model to optimize the plant behavior, by computing a sequence of future ma- nipulated variable adjustments. Usually the optimal control sequence is obtained by minimizing a multi-stage cost functional on the basis of open-loop predictions. The presence of uncertainty in the model used for the optimization raises the question of robustness, i.e., the maintenance of certain properties such as stability and performance in the presence of uncertainty. The need for guaranteeing the closed-loop stability in presence of uncertainties motivates the conception of robust nonlinear MPC, in which the perturbations are explicitly taken in account in the design of the controller. When the nature of the uncertainty is know, and it is assumed to be bounded in some compact set, the robust RH control can be determined, in a natural way, by solving a min–max optimal control problem, that is, the performance objective is optimized for the worst-case scenario. However, the use of min-max techniques is limited by the high computational burden required to solve the optimization problem. In the case of constrained system, a possibility to ensure the robust constraint satisfaction and the closed-loop stability without resorting to min-max optimization consists in imposing restricted (tightened) constraints on the the predicted trajectories during the optimization. In this framework, an MPC scheme with constraint tightening for discrete-time nonlinear systems affected by state-dependent and norm bounded uncertainties is proposed and discussed. A novel method to tighten the constraints relying on the nominal state prediction is described, leading to less conservative set contractions than in the existing approaches. Moreover, by imposing a stabilizing state constraint at the end of the control horizon (in place of the usual terminal one placed at the end of the prediction horizon), less stringent assumptions can be posed on the terminal region, while improving the robust stability properties of the MPC closed-loop system. The robust nonlinear MPC formulation with tightened constraints is then used to design off- line approximate feedback laws able to guarantee the practical stability of the closed-loop system. By using off-line approximations, the computational burden due to the on-line optimization is removed, thus allowing for the application of the MPC to systems with fast dynamics. In this framework, we will also address the problem of approximating possibly discontinuous feedback functions, thus overcoming the limitation of existent approximation scheme which assume the continuity of the RH control law (whereas this condition is not always verified in practice, due to both nonlinearities and constraints). Finally, the problem of stabilizing constrained systems with networked unreliable (and de- layed) feedback and command channels is also considered. In order to satisfy the control ob- jectives for this class of systems, also referenced to as Networked Control Systems (NCS’s), a control scheme based on the combined use of constraint tightening MPC with a delay compen- sation strategy will be proposed and analyzed. The stability properties of all the aforementioned MPC schemes are characterized by using the regional Input-to-State Stability (ISS) tool. The ISS approach allows to analyze the depen- dence of state trajectories of nonlinear systems on the magnitude of inputs, which can represent control variables or disturbances. Typically, in MPC the ISS property is characterized in terms of Lyapunov functions, both for historical and practical reasons, since the optimal finite horizon cost of the optimization problem can be easily used for this task. Note that, in order to study the ISS property of MPC closed-loop systems, global results are in general not useful because, due to the presence of state and input constraints, it is impossible to establish global bounds for the multi-stage cost used as Lyapunov function. On the other hand local results do not allow to analyze the properties of the predictive control law in terms of its region of attraction. There- fore, regional ISS results have to employed for MPC controlled systems. Moreover, in the case of NCS, the resulting control strategy yields to a time-varying closed-loop system, whose stability properties can be analyzed using a novel regional ISS characterization in terms of time-varying Lyapunov functions.
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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation". Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.
Texto completoBangalore, Narendranath Rao Amith Kaushal. "Online Message Delay Prediction for Model Predictive Control over Controller Area Network". Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.
Texto completoMaster of Science
Libros sobre el tema "Networked Model Predictive Control"
Zou, Yuanyuan y Shaoyuan Li. Distributed Cooperative Model Predictive Control of Networked Systems. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6084-0.
Texto completoChristofides, Panagiotis D., Jinfeng Liu y David Muñoz de la Peña. Networked and Distributed Predictive Control. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8.
Texto completoCamacho, E. F. y C. Bordons. Model Predictive control. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5.
Texto completoZhang, Ridong, Anke Xue y Furong Gao. Model Predictive Control. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0083-7.
Texto completoCamacho, Eduardo F. y Carlos Bordons. Model Predictive Control. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8.
Texto completoKouvaritakis, Basil y Mark Cannon. Model Predictive Control. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24853-0.
Texto completo1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.
Buscar texto completoCamacho, E. F. Model predictive control. London: Springer, 2003.
Buscar texto completoCamacho, E. F. Model predictive control. 2a ed. New York: Springer, 2004.
Buscar texto completoMaurice, Heemels y Johansson Mikael, eds. Networked control systems. Berlin: Springer, 2010.
Buscar texto completoCapítulos de libros sobre el tema "Networked Model Predictive Control"
Bemporad, Alberto y Davide Barcelli. "Decentralized Model Predictive Control". En Networked Control Systems, 149–78. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-033-5_5.
Texto completoChristofides, Panagiotis D., Jinfeng Liu y David Muñoz de la Peña. "Lyapunov-Based Model Predictive Control". En Networked and Distributed Predictive Control, 13–45. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_2.
Texto completoChristofides, Panagiotis D., Jinfeng Liu y David Muñoz de la Peña. "Multirate Distributed Model Predictive Control". En Networked and Distributed Predictive Control, 193–218. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_6.
Texto completoJurado, Isabel y Pablo Millán. "Asynchronous Packetized Model Predictive Control". En Asynchronous Control for Networked Systems, 133–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21299-9_6.
Texto completoPin, Gilberto y Thomas Parisini. "Stabilization of Networked Control Systems by Nonlinear Model Predictive Control: A Set Invariance Approach". En Nonlinear Model Predictive Control, 195–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_15.
Texto completoChristofides, Panagiotis D., Jinfeng Liu y David Muñoz de la Peña. "Distributed Model Predictive Control: Two-Controller Cooperation". En Networked and Distributed Predictive Control, 99–133. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_4.
Texto completoChristofides, Panagiotis D., Jinfeng Liu y David Muñoz de la Peña. "Distributed Model Predictive Control: Multiple-Controller Cooperation". En Networked and Distributed Predictive Control, 135–92. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_5.
Texto completoGrüne, L., F. Allgöwer, R. Findeisen, J. Fischer, D. Groß, U. D. Hanebeck, B. Kern et al. "Distributed and Networked Model Predictive Control". En Control Theory of Digitally Networked Dynamic Systems, 111–67. Heidelberg: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01131-8_4.
Texto completoFindeisen, R. y P. Varutti. "Stabilizing Nonlinear Predictive Control over Nondeterministic Communication Networks". En Nonlinear Model Predictive Control, 167–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_13.
Texto completoZou, Yuanyuan y Shaoyuan Li. "Self-Triggered DMPC of Networked Systems". En Distributed Cooperative Model Predictive Control of Networked Systems, 105–23. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6084-0_6.
Texto completoActas de conferencias sobre el tema "Networked Model Predictive Control"
Kloock, Maximilian, Patrick Scheffe, Lukas Botz, Janis Maczijewski, Bassam Alrifaee y Stefan Kowalewski. "Networked Model Predictive Vehicle Race Control". En 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917222.
Texto completoZhong-Hua Pang y Guo-Ping Liu. "Model-based recursive networked predictive control". En 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642322.
Texto completoVarutti, P., B. Kern, T. Faulwasser y R. Findeisen. "Event-based model predictive control for Networked Control Systems". En 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400921.
Texto completoLiu, Xiao-Hua y Jian Li. "Robust model predictive control for constrained networked control system". En Education (ITIME). IEEE, 2009. http://dx.doi.org/10.1109/itime.2009.5236286.
Texto completoZhiyuan Yao, Ye Hu y Nael H. El-Farra. "Networked model predictive control of spatially distributed processes". En 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580139.
Texto completoGuofeng Zhang, Xiang Chen y Tongwen Chen. "A model predictive control approach to networked systems". En 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434214.
Texto completoWang, Quan, Yuanyuan Zou y Yugang Niu. "Event-triggered model predictive control for wireless networked control system". En 2014 International Conference on Mechatronics and Control (ICMC). IEEE, 2014. http://dx.doi.org/10.1109/icmc.2014.7231573.
Texto completoHashimoto, Kazumune, Shuichi Adachi y Dimos V. Dimarogonas. "Self-triggered nonlinear model predictive control for networked control systems". En 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171995.
Texto completoBianchi, D., A. Ferrara y M. D. Di Benedetto. "Adaptive networked model predictive control of freeway traffic systems". En 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6579872.
Texto completoIrwin, G. W. "Predictive control using multiple model networks". En IEE Colloquium on Model Predictive Control: Techniques and Applications Day 1. IEE, 1999. http://dx.doi.org/10.1049/ic:19990533.
Texto completoInformes sobre el tema "Networked Model Predictive Control"
Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones y K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), septiembre de 1994. http://dx.doi.org/10.2172/10178912.
Texto completoCastanon, David A. y Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2002. http://dx.doi.org/10.21236/ada409519.
Texto completoB. Wayne Bequette y Priyadarshi Mahapatra. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants. Office of Scientific and Technical Information (OSTI), agosto de 2010. http://dx.doi.org/10.2172/1026486.
Texto completoLi, Dinggen y Yang Ye. The Control of Air-Fuel Ratio of the Engine Based on Model Predictive Control. Warrendale, PA: SAE International, octubre de 2012. http://dx.doi.org/10.4271/2012-32-0050.
Texto completoOllerenshaw, Douglas y Mark Costello. Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2005. http://dx.doi.org/10.21236/ada432823.
Texto completoBouffard, Patrick. On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2012. http://dx.doi.org/10.21236/ada572108.
Texto completoAswani, Anil, Humberto Gonzalez, S. S. Sastry y Claire Tomlin. Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2011. http://dx.doi.org/10.21236/ada558989.
Texto completoHaves, Phillip, Brandon Hencey, Francesco Borrell, John Elliot, Yudong Ma, Brian Coffey, Sorin Bengea y Michael Wetter. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced. Office of Scientific and Technical Information (OSTI), junio de 2010. http://dx.doi.org/10.2172/988177.
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Texto completoMiddlebrooks, Sam E. y Brian J. Stankiewicz. Toward the Development of a Predictive Computer Model of Decision Making During Uncertainty for Use in Simulations of U.S. Army Command and Control System. Fort Belvoir, VA: Defense Technical Information Center, enero de 2006. http://dx.doi.org/10.21236/ada443462.
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