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dc.contributor.author
Giovanini, Leonardo Luis
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dc.contributor.author
Sanchez, Guido Marcelo
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dc.contributor.author
Murillo, Marina Hebe
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dc.contributor.author
Limache, Alejandro Cesar
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dc.contributor.other
Zheng, Tao
dc.date.available
2020-07-31T14:07:49Z
dc.date.issued
2011
dc.identifier.citation
Giovanini, Leonardo Luis; Sanchez, Guido Marcelo; Murillo, Marina Hebe; Limache, Alejandro Cesar; Distributed Model Predictive Control Based on Dynamics Games; IntechOpen; 2011; 65-90
dc.identifier.isbn
978-953-307-298-2
dc.identifier.uri
http://hdl.handle.net/11336/110632
dc.description.abstract
Model predictive control (MPC) is widely recognized as a high performance, yet practical,control technology. This model-based control strategy solves at each sample a discrete-timeoptimal control problem over a finite horizon, producing a control input sequence. Anattractive attribute of MPC technology is its ability to systematically account for systemconstraints. The theory of MPC for linear systems is well developed; all aspects suchas stability, robustness,feasibility and optimality have been extensively discussed in theliterature (see, e.g., (Bemporad & Morari, 1999; Kouvaritakis & Cannon, 2001; Maciejowski, 2002; Mayne et al., 2000)). The effectiveness of MPC depends on model accuracy and the availability of fast computational resources. These requirements limit the application base for MPC. Even though, applications abound in process industries (Camacho & Bordons, 2004), manufacturing (Braun et al., 2003), supply chains (Perea-Lopez et al., 2003), among others, are becoming more widespread.Two common paradigms for solving system-wide MPC calculations are centralised anddecentralised strategies. Centralised strategies may arise from the desire to operate thesystem in an optimal fashion, whereas decentralised MPC control structures can result fromthe incremental roll-out of the system development. An effective centralised MPC can bedifficult, if not impossible to implement in large-scale systems (Kumar & Daoutidis, 2002;Lu, 2003). In decentralised strategies, the system-wide MPC problem is decomposed intosubproblems by taking advantage of the system structure, and then, these subproblemsare solved independently. In general, decentralised schemes approximate the interactionsbetween subsystems and treat inputs in other subsystems as external disturbances. Thisassumption leads to a poor systemperformance (Sandell Jr et al., 1978; ?iljak, 1996). Therefore, there is a need for a cross-functional integration between the decentralised controllers, in which a coordination level performs steady-state target calculation for decentralised controller (Aguilera & Marchetti, 1998; Aske et al., 2008; Cheng et al., 2007; 2008; Zhu & Henson, 2002).Several distributed MPC formulations are available in the literature. A distributed MPCframework was proposed by Dumbar and Murray (Dunbar & Murray, 2006) for the classof systems that have independent subsystem dynamic but link through their cost functionsand constraints. Then, Dumbar (Dunbar, 2007) proposed an extension of this framework thathandles systemswith weakly interacting dynamics. Stability is guaranteed through the use ofa consistency constraint that forces the predicted and assumed input trajectories to be close toeach other. The resulting performance is different from centralised implementations in mostof cases. Distributed MPC algorithms for unconstrained and LTI systems were proposed in(Camponogara et al., 2002; Jia & Krogh, 2001; Vaccarini et al., 2009; Zhang & Li, 2007). In (Jia & Krogh, 2001) and (Camponogara et al., 2002) the evolution of the states of each subsystem is assumed to be only influenced by the states of interacting subsystems and local inputs, while these restrictions were removed in (Jia & Krogh, 2002; Vaccarini et al., 2009; Zhang & Li, 2007). This choice of modelling restricts the system where the algorithm can be applied, because inmany cases the evolution of states is also influenced by the inputs of interconnected subsystems. More critically for these frameworks is the fact that subsystems-based MPCs only know the cost functions and constraints of their subsystem. However, stability and optimality as well as the effect of communication failures has not been established.The distributed model predictive control problem from a game theory perspective for LTIsystems with general dynamical couplings, and the presence of convex coupled constraintsis addressed. The original centralised optimisation problem is transformed in a dynamicgame of a number of local optimisation problems, which are solved using the relevantdecision variables of each subsystem and exchanging information in order to coordinatetheir decisions. The relevance of proposed distributed control scheme is to reduce thecomputational burden and avoid the organizational obstacles associated with centralisedimplementations, while retains its properties (stability, optimality, feasibility). In this context,the type of coordination that can be achieved is determined by the connectivity and capacity of the communication network as well as the information available of system?s cost function and constraints. In this work we will assume that the connectivity of the communication network is sufficient for the subsystems to obtain information of all variables that appear in their local problems. We will show that when system?s cost function and constraints are known by all distributed controllers, the solution of the iterative process converge to the centralised MPC solution. This means that properties (stability, optimality, feasibility) of the solution obtained using the distributed implementation are the same ones of the solution obtained using the centralised implementation. Finally, the effects of communication failures on the system?s properties (convergence, stability and performance) are studied. We will show the effect of the system partition and communication on convergence and stability, and we will find a upper bound of the system performance.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
IntechOpen
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DISTRIBUTED-MPC
dc.subject
GAMES
dc.subject
MPC
dc.subject.classification
Sistemas de Automatización y Control
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Distributed Model Predictive Control Based on Dynamics Games
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/bookPart
dc.type
info:ar-repo/semantics/parte de libro
dc.date.updated
2020-07-20T16:20:02Z
dc.journal.pagination
65-90
dc.journal.pais
Croacia
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dc.description.fil
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Sanchez, Guido Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Murillo, Marina Hebe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
dc.description.fil
Fil: Limache, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.intechopen.com/books/advanced-model-predictive-control
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/books/advanced-model-predictive-control/distributed-model-predictive-control-based-on-dynamic-games
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.5772/16268
dc.conicet.paginas
430
dc.source.titulo
Advanced Model Predictive Control
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