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Статті в журналах з теми "Explicit Approximated Model Predictive Control"
Pregelj, Boštjan, and Samo Gerkšič. "Hybrid explicit model predictive control of a nonlinear process approximated with a piecewise affine model." Journal of Process Control 20, no. 7 (August 2010): 832–39. http://dx.doi.org/10.1016/j.jprocont.2010.05.002.
Повний текст джерелаGrancharova, Alexandra, and Tor A. Johansen. "Reduced Dimension Approach to Approximate Explicit Model Predictive Control." IFAC Proceedings Volumes 36, no. 18 (September 2003): 531–36. http://dx.doi.org/10.1016/s1474-6670(17)34723-7.
Повний текст джерелаBussell, E. H., and N. J. Cunniffe. "Applying optimal control theory to a spatial simulation model of sudden oak death: ongoing surveillance protects tanoak while conserving biodiversity." Journal of The Royal Society Interface 17, no. 165 (April 2020): 20190671. http://dx.doi.org/10.1098/rsif.2019.0671.
Повний текст джерелаJohansen, T. A., and A. Grancharova. "Approximate explicit constrained linear model predictive control via orthogonal search tree." IEEE Transactions on Automatic Control 48, no. 5 (May 2003): 810–15. http://dx.doi.org/10.1109/tac.2003.811259.
Повний текст джерелаJohansen, Tor A., and Alexandra Grancharova. "APPROXIMATE EXPLICIT MODEL PREDICTIVE CONTROL IMPLEMENTED VIA ORTHOGONAL SEARCH TREE PARTITIONING." IFAC Proceedings Volumes 35, no. 1 (2002): 195–200. http://dx.doi.org/10.3182/20020721-6-es-1901.00601.
Повний текст джерелаSun, Weiquan, Min Li, and Kexin Wang. "Approximate explicit model predictive control using high-level canonical piecewise-affine functions." International Journal of Automation and Control 6, no. 1 (2012): 66. http://dx.doi.org/10.1504/ijaac.2012.045441.
Повний текст джерелаBakaráč, Peter, and Michal Kvasnica. "Approximate explicit robust model predictive control of a CSTR with fast reactions." Chemical Papers 73, no. 3 (November 9, 2018): 611–18. http://dx.doi.org/10.1007/s11696-018-0630-4.
Повний текст джерелаCharitopoulos, Vassilis M., Lazaros G. Papageorgiou, and Vivek Dua. "Multi Set-Point Explicit Model Predictive Control for Nonlinear Process Systems." Processes 9, no. 7 (July 2, 2021): 1156. http://dx.doi.org/10.3390/pr9071156.
Повний текст джерелаKiš, Karol, and Martin Klaučo. "Neural network based explicit MPC for chemical reactor control." Acta Chimica Slovaca 12, no. 2 (October 1, 2019): 218–23. http://dx.doi.org/10.2478/acs-2019-0030.
Повний текст джерелаŁawryńczuk, Maciej, Piotr M. Marusak, Patryk Chaber, and Dawid Seredyński. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods." Energies 15, no. 7 (March 28, 2022): 2483. http://dx.doi.org/10.3390/en15072483.
Повний текст джерелаДисертації з теми "Explicit Approximated Model Predictive Control"
Filippo, 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.
Повний текст джерелаOne 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).
XXIII Ciclo
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Barsk, Karl-Johan. "Model Predictive Control of a Tricopter." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79066.
Повний текст джерелаFelipe, Dominguez Luis Felipe Dominguez. "Advances in multiparametric nonlinear programming & explicit model predictive control." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536023.
Повний текст джерелаLambert, Romain. "Approximation methodologies for explicit model predictive control of complex systems." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/13943.
Повний текст джерелаRivotti, Pedro. "Multi-parametric programming and explicit model predictive control of hybrid systems." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24432.
Повний текст джерелаKrieger, Alexandra. "Modelling, optimisation and explicit model predictive control of anaesthesia drug delivery systems." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23908.
Повний текст джерелаHellström, Erik. "Explicit use of road topography for model predictive cruise control in heavy trucks." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2843.
Повний текст джерелаNew and exciting possibilities in vehicle control are revealed by the consideration of topography through the combination GPS and three dimensional road maps. This thesis explores how information about future road slopes can be utilized in a heavy truck with the aim at reducing the fuel consumption over a route without increasing the total travel time.
A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function is used to define the optimization criterion. Through the function parameters the user is enabled to decide how fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use are weighed.
Computer simulations with a load of 40 metric tons shows that the fuel consumption can be reduced with 2.5% with a negligible change in travel time, going from Link¨oping to J¨onk¨oping and back. The road slopes are calculated by differentiation of authentic altitude measurements along this route. The complexity of the algorithm when achieving these results allows the simulations to run two to four times faster than real time on a standard PC, depending on the desired update frequency of the control signals.
Bolin, Tobias. "Nonlinear Approximative Explicit Model Predictive Control Through Neural Networks : Characterizing Architectures and Training Behavior." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264994.
Повний текст джерелаModell-prediktiv reglering (MPC, efter engelskans Model Predictive Control) är ett paradigm inom reglertekniken som på ett effektivt sätt kan hantera begränsningar i systemet som ska regleras. Den här egenskapen kommer på bekostnad av att MPC kräver mycket datorkraft. Tidigare har användning av den här typen av kontroller därför varit begränsad till långsamma system. På senare tid har framsteg inom hård- och mjukvara dock möjliggjort användning av MPC på inbyggda system. Där kan dess förmåga att hantera begränsningar användas för att minska slitage, öka effektivitet och förbättra prestanda inom allt från bilar till vindkraftverk. Ett sätt att minska beräkningsbördan ytterligare är att beräkna MPC-policyn i förväg och spara den i en tabell. Det här tillvägagångssättet kallas explicit MPC. Ett alternativt tillvägagångssätt är att träna ett neuralt nätverk till att approximera policyn. Potentiellt har det här fördelarna att ett neuralt nätverk inte är begränsat till att efterlikna policys för system med linjär dynamik, och att det finns resultat som pekar på att neurala nätverk är väl lämpade för att lagra policys för explicit MPC. En begränsad mängd arbete har gjorts inom det här området. Hur nätverken designas och tränas tenderar därför att reflektera trender inom andra applikationsområden för neurala nätverk istället för att baseras på vad som fungerar för att implementera MPC. Det här examensarbetet försöker avhjälpa det här problemet. Dels genom en litteraturstudie och dels genom att undersöka hur olika arkitekturer för neurala nätverk beter sig när de tränas för att efterlikna en ickelinjär MPC-kontroller som ska stabilisera en inverterad pendel. Resultaten tyder på att nätverk med ReLU-aktivering ger bättre prestanda än motsvarande nätverk som använder SELU eller tangens hyperbolicus som aktiveringsfunktion. Resultaten visar också att batch noralization och dropout försämmrar nätverkens förmåga att lära sig policyn och att prestandan blir bättre om antalet lager i nätverket ökar. De neurala nätverken uppvisar dock i vissa fall kvalitativa problem, så som statiska fel och oscillerande kontrollsignaler nära begränsningar.
Volker, Anna. "Explicit/multi-parametric moving horizon estimation and model : predictive control & application to small unmanned aerial vehicles." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538787.
Повний текст джерелаVoelker, Anna. "Explicit/multi-parametric moving horizon estimation and model predictive control & their application to small unmanned aerial vehicles." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7030.
Повний текст джерелаКниги з теми "Explicit Approximated Model Predictive Control"
Grancharova, Alexandra, and Tor Arne Johansen. Explicit Nonlinear Model Predictive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0.
Повний текст джерелаGrancharova, Alexandra. Explicit Nonlinear Model Predictive Control: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Знайти повний текст джерелаExplicit Nonlinear Model Predictive Control Theory And Applications. Springer, 2012.
Знайти повний текст джерелаЧастини книг з теми "Explicit Approximated Model Predictive Control"
Grancharova, Alexandra, and Tor Arne Johansen. "Explicit NMPC via Approximate mp-NLP." In Explicit Nonlinear Model Predictive Control, 87–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_4.
Повний текст джерелаGrancharova, Alexandra, and Tor A. Johansen. "Explicit Approximate Model Predictive Control of Constrained Nonlinear Systems with Quantized Input." In Nonlinear Model Predictive Control, 371–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_30.
Повний текст джерелаJones, Colin N., and Manfred Morari. "High-Speed Model Predictive Control: An Approximate Explicit Approach." In Three Decades of Progress in Control Sciences, 233–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11278-2_16.
Повний текст джерелаMaeder, Urban, Raphael Cagienard, and Manfred Morari. "Explicit Model Predictive Control." In Lecture Notes in Control and Information Sciences, 237–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37010-9_8.
Повний текст джерелаBemporad, Alberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 405–11. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_10.
Повний текст джерелаBemporad, PhDAlberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 1–9. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5102-9_10-1.
Повний текст джерелаBemporad, Alberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 1–7. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-5102-9_10-2.
Повний текст джерелаBemporad, Alberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 744–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_10.
Повний текст джерелаKouvaritakis, Basil, and Mark Cannon. "Explicit Use of Probability Distributions in SMPC." In Model Predictive Control, 303–41. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24853-0_8.
Повний текст джерелаGrancharova, Alexandra, and Tor Arne Johansen. "Nonlinear Model Predictive Control." In Explicit Nonlinear Model Predictive Control, 39–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_2.
Повний текст джерелаТези доповідей конференцій з теми "Explicit Approximated Model Predictive Control"
Nam, Nguyen Hoai, Sorin Olaru, and Morten Hovd. "Patchy approximate explicit model predictive control." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5670223.
Повний текст джерелаChen, Y., S. Li, and N. Li. "Complexity reduced explicit model predictive control by solving approximated mp-QP program." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244434.
Повний текст джерелаBinder, Matthias, Georgios Darivianakis, Annika Eichler, and John Lygeros. "Approximate Explicit Model Predictive Controller using Gaussian Processes." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029942.
Повний текст джерелаZaninit, Francesco, Colin N. Jones, David Atienza, and Giovanni De Micheli. "Multicore thermal management using approximate explicit model predictive control." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537891.
Повний текст джерелаGrancharova, Alexandra, and Tor A. Johansen. "Explicit Approximate Approach to Feedback Min-Max Model Predictive Control of Constrained Nonlinear Systems." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377772.
Повний текст джерелаHuang, Mike, Ken Butts, and Ilya Kolmanovsky. "A Low Complexity Gain Scheduling Strategy for Explicit Model Predictive Control of a Diesel Air Path." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9754.
Повний текст джерелаSowman, J., D. S. Laila, and S. Longo. "Real-time approximate explicit nonlinear model predictive control for the swing-up of a reaction wheel pendulum." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402891.
Повний текст джерелаKnyazev, Andrew, Peizhen Zhu, and Stefano Di Cairano. "Explicit model predictive control accuracy analysis." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402565.
Повний текст джерелаWang, Kai, Yuning Jiang, Juraj Oravec, Mario E. Villanueva, and Boris Houska. "Parallel Explicit Tube Model Predictive Control." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029177.
Повний текст джерелаFalugi, P., S. Olaru, and D. Dumur. "Explicit robust multi-model predictive control." In Automation (MED 2008). IEEE, 2008. http://dx.doi.org/10.1109/med.2008.4602102.
Повний текст джерелаЗвіти організацій з теми "Explicit Approximated Model Predictive Control"
An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0741.
Повний текст джерела