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Academic literature on the topic 'Networked Model Predictive Control'

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Published: 11 March 2023

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Journal articles on the topic "Networked Model Predictive Control"

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Wu, Jing, Liqian Zhang, and Tongwen Chen. "Model predictive control for networked control systems." International Journal of Robust and Nonlinear Control 19, no. 9 (June 2009): 1016–35. http://dx.doi.org/10.1002/rnc.1361.

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Onat, Ahmet, A. Teoman Naskali, and Emrah Parlakay. "Model Based Predictive Networked Control Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 13000–13005. http://dx.doi.org/10.3182/20080706-5-kr-1001.02198.

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Vaccarini, Massimo, Sauro Longhi, and M. Reza Katebi. "Unconstrained networked decentralized model predictive control." Journal of Process Control 19, no. 2 (February 2009): 328–39. http://dx.doi.org/10.1016/j.jprocont.2008.03.005.

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He, Fang, Xiao Li, and Qiang Wang. "Study of Predictive Control in Industrial Networked Control System." Advanced Materials Research 201-203 (February 2011): 2087–90. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2087.

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In order to improve the performance of networked control system which has the characteristic of uncertain time delay, predictive control is adopted. The model of networked control system is analyzed. Take into account uncertain network time delay caused by various factors and problem of model mismatch, predictive controller is designed and simulation model of system is built using software of Matlab. A method of reducing model mismatch is proposed. Study results show that predictive control can make networked control system with good performance.
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Ulusoy, Alphan, Ahmet Onat, and Ozgur Gurbuz. "Wireless Model Based Predictive Networked Control System." IFAC Proceedings Volumes 42, no. 3 (2009): 40–47. http://dx.doi.org/10.3182/20090520-3-kr-3006.00007.

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Ewald, Grzegorz, and Mietek A. Brdys. "Model Predictive Controller for Networked Control Systems." IFAC Proceedings Volumes 43, no. 8 (2010): 274–79. http://dx.doi.org/10.3182/20100712-3-fr-2020.00046.

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Chen, Zai Ping, and Xue Wang. "Research on Networked Control Systems Based on Adaptive Predictive Control." Applied Mechanics and Materials 441 (December 2013): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amm.441.833.

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According to the random time-delay exist in sensor-controller channel and controller-actuator channel in networked control systems, an adaptive predictive control strategy was proposed. In this control strategy, an improved generalized predictive control algorithm is adopted to compensate the networked random time-delay. In addition, using the recursive least squares with a variable forgetting factor algorithm to indentify the model parameters of controlled object on-line, through the way, it could adjust the systems with unknown parameters adaptively. Simulation results show that the adaptive predictive control proposed could solve random time-delay of networked control systems effectively.
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Zhang, Yingwei, Xue Chen, and Renquan Lu. "Performance of Networked Control Systems." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/382934.

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Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs) with dual-network are designed by model predictive control method. The contributions are as follows. (1) The predictive control problem of the dual-network is considered. (2) The predictive performance of the dual-network is evaluated. (3) Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability. Finally, the simulation results show the feasibility and effectiveness of the controllers designed.
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Kamal, Faria, and Badrul Chowdhury. "Model predictive control and optimization of networked microgrids." International Journal of Electrical Power & Energy Systems 138 (June 2022): 107804. http://dx.doi.org/10.1016/j.ijepes.2021.107804.

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Bernardini, D., M. C. F. Donkers, A. Bemporad, and W. P. M. H. Heemels. "A Model Predictive Control Approach for Stochastic Networked Control Systems." IFAC Proceedings Volumes 43, no. 19 (2010): 7–12. http://dx.doi.org/10.3182/20100913-2-fr-4014.00007.

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Dissertations / Theses on the topic "Networked Model Predictive Control"

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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.

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We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.
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Qiu, Quanwei. "Networked Model Predictive Control for Microgrids with Distributed PV Generators." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/400460.

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More and more renewable energy sources are being integrated into microgrids—and while this causes many control challenges for microgrids, it can also yield numerous economic and environmental benefits. Therefore, it is necessary to develop proper control schemes for microgrids to address the different control issues in their hierarchical structure while adapting to the different time scales of the three control levels. Conversely, because model predictive control (MPC) has significant advantages—the inclusion of forecasts, the simplicity of the algorithm, and the flexibility to handle hard constraints—it has attracted significant attention in industrial control systems. Motivated by these factors, this research focuses on implementing MPC techniques in microgrids, which are solely supplied by photovoltaic (PV) generators, to address different control problems. For primary control of the microgrid hierarchy, which is mainly responsible for the inner control of the local distributed generation units, MPC can be applied to control of the power converters that serve as interfaces between the sources and the loads. Therefore, in this control level, a novel output-feedback MPC technique based on ellipsoidal set-membership state estimation is designed for a direct current to direct current (DC-DC) converter, considering the unknown-but-bounded external disturbances. A long-horizon finite-states (FS) MPC strategy is designed for the direct current to alternating current (DC-AC) inverter to reduce the sampling and switching frequency through a multi-step implementation approach and a control sequence rearrangement method. For secondary control, which is in charge of the compensation for the frequency and voltage deviations and is usually communication-based, the distributed MPC strategy can be used to realize the desired cooperative control among the geographically dispersed units. Thus, a novel distributed model predictive controller is developed to enhance system performance. It takes into account the fact that the distributed controllers’ communication network might be subject to switching topology due to the disconnection and reconnection of controllers, random failures, and recoveries of the links between controllers. A Markov chain with a time-varying probability transition matrix is used to describe the stochastic topology evolution of the control network. Tertiary control is used to coordinate the power flow between the microgrid and the utility grid and offers economic operations for microgrids. Since the integration of renewable energy sources causes low inertia and power fluctuation in microgrids, battery energy storage is essential to addressing these issues. To coordinate the charging/discharging schedule of the battery storage units, a networked MPC strategy can be adopted to realize the communication between different microgrid components and make use of the forecasts for PV power generation and load demand. The multi-microgrid system is considered subject to partial fault because of non-functional generators, batteries, or even transmission lines in this research. Hence, both the connection status of the electrical grid and the communication network are incorporated into the system modeling. In addition, the set-membership estimation is adopted to deal with the possible state unavailability caused by non-functional batteries or communication failures. In the theoretical section of this thesis, different sufficient conditions are established to ensure the stability of the investigated systems, and the optimal control inputs are obtained by solving the corresponding optimization problems. For easy implementation with MATLAB solvers, all the constraints and conditions of the optimization problems are transformed into linear matrix inequalities. Different recursive MPC algorithms are designed to control the target systems, and some extended algorithms are also developed to assist with the computation to determine the optimal solutions. In the demonstration section of this thesis, the designed controllers are all implemented in the numerical simulations or Simulink tests to verify their effectiveness, and an experimental test based on Raspberry Pi is conducted to demonstrate the wireless communication employing the designed networked model predictive controller.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Eng & Built Env
Science, Environment, Engineering and Technology
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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.

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Wireless networks in industrial process control enable new system architectures and designs. However, wireless control systems can be severely affected by the imperfections of the communication links. This thesis proposes new methods to handle such imperfections by adding additional components in the control loop, or by adapting sampling intervals and control actions. First, the predictive outage compensator is proposed. It is a filter which is implemented at the receiver side of networked control systems. There it generates predicted samples when data are lost, based on past data. The implementation complexity of the predictive outage compensator is analyzed. Simulation and experimental results show that it can considerably improve the closed-loop control performance under communication losses. The thesis continues with presenting an algorithm for controlling multiple processes on a shared communication network, using adaptive sampling intervals. The methodology is based on model predictive control, where the controller jointly decides the optimal control signal to be applied as well as the optimal time to wait before taking the next sample. The approach guarantees conflict-free network transmissions for all controlled processes. Simulation results show that the presented control law reduces the required amount of communication, while maintaining control performance. The third contribution of the thesis is an event-triggered model predictive controller for use over a wireless link. The controller uses open-loop optimal control, re-computed and communicated only when the system behavior deviates enough from a prediction. Simulations underline the methods ability to significantly reduce computation and communication effort, while guaranteeing a desired level of system performance. The thesis is concluded by an experimental validation of wireless control for a physical lab process. A hybrid model predictive controller is used, connected to the physical system through a wireless medium. The results reflect the advantages and challenges in wireless control.

QC 20140217

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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.

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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.

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2009/2010
One 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
1977
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Alrifaee, Bassam [Verfasser], Dirk [Akademischer Betreuer] Abel, and 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.

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Winqvist, 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.

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Model Predictive Control (MPC) is an optimization-based paradigm forfeedback control. The MPC relies on a dynamical model to make predictionsfor the future values of the controlled variables of the system. It then solvesa constrained optimization problem to calculate the optimal control actionthat minimizes the difference between the predicted values and the desiredor set values. One of the main limitations of the traditional MPC lies in thehigh computational cost resulting from solving the associated optimizationproblem online. Various offline strategies have been proposed to overcomethis, ranging from the explicit MPC (eMPC) to the recent learning-basedneural network approaches. This thesis investigates a framework for thetraining and evaluation of a neural network for learning to implement theMPC. As a part of the framework, a new approach for efficient generationof training data is proposed. Four different neural network structures arestudied; one of them is a black box network while the other three employMPC specific information. The networks are evaluated in terms of twodifferent performance metrics through experiments conducted on realistictwo-dimensional and four-dimensional systems. The experiments revealthat while using MPC specific structure in the neural networks resultsin performance gains when the training data is limited, all the networkstructures perform similarly as extensive training data is used. They furthershow that a recurrent neural network structure trained on both the state andcontrol trajectories of a family of MPCs is able to generalize to previouslyunseen MPC problems. The proposed methods in this thesis act as a firststep towards developing a coherent framework for characterization of learningapproaches in terms of both model validation and efficient training datageneration.
Modell-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.
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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.

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2007/2008
Nonlinear 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.
XXI Ciclo
1980
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9

Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluations required in different parts of the NMPC algorithm. In this work, the problem is tackled using a recently introduced efficient tool, the automatic differentiation (AD). Using the AD tool, a function is evaluated together with all its partial derivative from the code defining the function with machine accuracy. A new NMPC algorithm based on nonlinear least square optimization is proposed. In a first–order method, the sensitivity equations are integrated using a linear formula while the AD tool is applied to get their values accurately. For higher order approximations, more terms of the Taylor expansion are used in the integration for which the AD is effectively used. As a result, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear optimization can be efficiently solved. In many real control cases, the states are not measured and have to be estimated for each instance when a solution of the model equations is needed. A nonlinear extended version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose. The AD tool is used to calculate the required derivatives in the local linearization step of the filter automatically and accurately. Offset is another problem faced in NMPC. A new nonlinear integration is devised for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct the plant/model mismatch. The time response of the controller is also improved as a by–product. The proposed NMPC algorithm has been applied to an evaporation process and a two continuous stirred tank reactor (two–CSTR) process with satisfactory results to cope with large setpoint changes, unmeasured severe disturbances, and process/model mismatches. When the process equations are not known (black–box) or when these are too complicated to be used in the controller, modelling is needed to create an internal model for the controller. In this thesis, a continuous time recurrent neural network (CTRNN) in a state–space form is developed to be used in NMPC context. An efficient training algorithm for the proposed network is developed using AD tool. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve online the optimization problem of the NMPC. The proposed CTRNN and the predictive controller were tested on an evaporator and two–CSTR case studies. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is implemented to control the system. In this work a nonlinear state–space class Wiener model is used to identify the black–box model of the gasifier. A linear model of the plant at zero–load is adopted as a base model for prediction. Then, a feedforward neural network is created as the static gain for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behavior observed in open–loop simulations. By linearizing the neural network at each sampling time, the static nonlinear gain provides certain adaptation to the linear base model. The AD tool is used here to linearize the neural network efficiently. Noticeable performance improvement is observed when compared with pure linear MPC. The controller was able to pass all tests specified in the benchmark problem at all load conditions.
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Bangalore, 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.

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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz ATmega328P microcontroller and measures the execution time overhead. The results clearly indicate that the method is computationally feasible for online usage.
Master of Science
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Books on the topic "Networked Model Predictive Control"

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Zou, Yuanyuan, and 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.

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Christofides, Panagiotis D., Jinfeng Liu, and 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.

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Camacho, E. F., and C. Bordons. Model Predictive control. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5.

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Zhang, Ridong, Anke Xue, and Furong Gao. Model Predictive Control. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0083-7.

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Camacho, Eduardo F., and Carlos Bordons. Model Predictive Control. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8.

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Kouvaritakis, Basil, and Mark Cannon. Model Predictive Control. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24853-0.

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1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.

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Camacho, E. F. Model predictive control. London: Springer, 2003.

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Camacho, E. F. Model predictive control. 2nd ed. New York: Springer, 2004.

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Maurice, Heemels, and Johansson Mikael, eds. Networked control systems. Berlin: Springer, 2010.

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Book chapters on the topic "Networked Model Predictive Control"

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Bemporad, Alberto, and Davide Barcelli. "Decentralized Model Predictive Control." In Networked Control Systems, 149–78. London: Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-033-5_5.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Lyapunov-Based Model Predictive Control." In Networked and Distributed Predictive Control, 13–45. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_2.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Multirate Distributed Model Predictive Control." In Networked and Distributed Predictive Control, 193–218. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_6.

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Jurado, Isabel, and Pablo Millán. "Asynchronous Packetized Model Predictive Control." In Asynchronous Control for Networked Systems, 133–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21299-9_6.

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Pin, Gilberto, and Thomas Parisini. "Stabilization of Networked Control Systems by Nonlinear Model Predictive Control: A Set Invariance Approach." In Nonlinear Model Predictive Control, 195–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_15.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Distributed Model Predictive Control: Two-Controller Cooperation." In Networked and Distributed Predictive Control, 99–133. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_4.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Distributed Model Predictive Control: Multiple-Controller Cooperation." In Networked and Distributed Predictive Control, 135–92. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_5.

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Grüne, L., F. Allgöwer, R. Findeisen, J. Fischer, D. Groß, U. D. Hanebeck, B. Kern, et al. "Distributed and Networked Model Predictive Control." In 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.

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Findeisen, R., and P. Varutti. "Stabilizing Nonlinear Predictive Control over Nondeterministic Communication Networks." In Nonlinear Model Predictive Control, 167–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_13.

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Zou, Yuanyuan, and Shaoyuan Li. "Self-Triggered DMPC of Networked Systems." In 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.

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Conference papers on the topic "Networked Model Predictive Control"

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Kloock, Maximilian, Patrick Scheffe, Lukas Botz, Janis Maczijewski, Bassam Alrifaee, and Stefan Kowalewski. "Networked Model Predictive Vehicle Race Control." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917222.

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Zhong-Hua Pang and Guo-Ping Liu. "Model-based recursive networked predictive control." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642322.

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Varutti, P., B. Kern, T. Faulwasser, and R. Findeisen. "Event-based model predictive control for Networked Control Systems." In 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.

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Liu, Xiao-Hua, and Jian Li. "Robust model predictive control for constrained networked control system." In Education (ITIME). IEEE, 2009. http://dx.doi.org/10.1109/itime.2009.5236286.

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Zhiyuan Yao, Ye Hu, and Nael H. El-Farra. "Networked model predictive control of spatially distributed processes." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580139.

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Guofeng Zhang, Xiang Chen, and Tongwen Chen. "A model predictive control approach to networked systems." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434214.

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Wang, Quan, Yuanyuan Zou, and Yugang Niu. "Event-triggered model predictive control for wireless networked control system." In 2014 International Conference on Mechatronics and Control (ICMC). IEEE, 2014. http://dx.doi.org/10.1109/icmc.2014.7231573.

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Hashimoto, Kazumune, Shuichi Adachi, and Dimos V. Dimarogonas. "Self-triggered nonlinear model predictive control for networked control systems." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171995.

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Bianchi, D., A. Ferrara, and M. D. Di Benedetto. "Adaptive networked model predictive control of freeway traffic systems." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6579872.

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Irwin, G. W. "Predictive control using multiple model networks." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 1. IEE, 1999. http://dx.doi.org/10.1049/ic:19990533.

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Reports on the topic "Networked Model Predictive Control"

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Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones, and K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10178912.

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Castanon, David A., and Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada409519.

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B. Wayne Bequette and Priyadarshi Mahapatra. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants. Office of Scientific and Technical Information (OSTI), August 2010. http://dx.doi.org/10.2172/1026486.

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Li, Dinggen, and Yang Ye. The Control of Air-Fuel Ratio of the Engine Based on Model Predictive Control. Warrendale, PA: SAE International, October 2012. http://dx.doi.org/10.4271/2012-32-0050.

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Ollerenshaw, Douglas, and Mark Costello. Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada432823.

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Bouffard, Patrick. On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada572108.

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Aswani, Anil, Humberto Gonzalez, S. S. Sastry, and Claire Tomlin. Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada558989.

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Haves, Phillip, Brandon Hencey, Francesco Borrell, John Elliot, Yudong Ma, Brian Coffey, Sorin Bengea, and Michael Wetter. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/988177.

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Kumar, Aditya, and George Boutselis. Transient Efficiency Flexibility and Reliability Optimization of Coal-Fired Power Plants - Report on Model-Predictive Control Library Development. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1856859.

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Middlebrooks, Sam E., and 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, January 2006. http://dx.doi.org/10.21236/ada443462.

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