Thematische Bibliographien / Cooperative distributed control

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Auswahl der wissenschaftlichen Literatur zum Thema „Cooperative distributed control“

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Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Cooperative distributed control"

Stewart, Brett T., Aswin N. Venkat, James B. Rawlings, Stephen J. Wright, and Gabriele Pannocchia. "Cooperative distributed model predictive control." Systems & Control Letters 59, no. 8 (2010): 460–69. http://dx.doi.org/10.1016/j.sysconle.2010.06.005.

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MINAMI, Yuki, and Takateru KOSAKA. "1101 Distributed cooperative control of distributed generation systems." Proceedings of the Optimization Symposium 2012.10 (2012): _1101–1_—_1101–4_. http://dx.doi.org/10.1299/jsmeopt.2012.10.0__1101-1_.

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Nasirian, Vahidreza, Seyedali Moayedi, Ali Davoudi, and Frank L. Lewis. "Distributed Cooperative Control of DC Microgrids." IEEE Transactions on Power Electronics 30, no. 4 (2015): 2288–303. http://dx.doi.org/10.1109/tpel.2014.2324579.

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Morstyn, Thomas, Branislav Hredzak, and Vassilios G. Agelidis. "Distributed Cooperative Control of Microgrid Storage." IEEE Transactions on Power Systems 30, no. 5 (2015): 2780–89. http://dx.doi.org/10.1109/tpwrs.2014.2363874.

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Bereza, Robert, Linnea Persson, and Bo Wahlberg. "Distributed Model Predictive Control for Cooperative Landing." IFAC-PapersOnLine 53, no. 2 (2020): 15180–85. http://dx.doi.org/10.1016/j.ifacol.2020.12.2290.

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HAYASHI, Naoki, and Naoyuki HARA. "Distributed and Cooperative Control in Wind Farms." IEICE ESS Fundamentals Review 14, no. 3 (2021): 170–80. http://dx.doi.org/10.1587/essfr.14.3_170.

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Wang, Mianyu, Nagarajan Kandasamy, Allon Guez, and Moshe Kam. "Distributed Cooperative Control for Adaptive Performance Management." IEEE Internet Computing 11, no. 1 (2007): 31–39. http://dx.doi.org/10.1109/mic.2007.7.

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Lin, Jinghuai, Yongming Wang, Hongjie Deng, and Zhenguo Shao. "Distributed cooperative control strategy for islanded microgrids." Journal of Physics: Conference Series 1633 (September 2020): 012126. http://dx.doi.org/10.1088/1742-6596/1633/1/012126.

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Hamidi, R. Jalilzadeh, H. Livani, S. H. Hosseinian, and G. B. Gharehpetian. "Distributed cooperative control system for smart microgrids." Electric Power Systems Research 130 (January 2016): 241–50. http://dx.doi.org/10.1016/j.epsr.2015.09.012.

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Yague, Sauro J., Guillermo Reyes Carmenaty, Alejandro Rolán Blanco, and Aurelio García Cerrada. "Distributed Cooperative Control for Stepper Motor Synchronization." MATEC Web of Conferences 167 (2018): 02001. http://dx.doi.org/10.1051/matecconf/201816702001.

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This paper describes the design and simulation of a distributed cooperative control algorithm based on multi-agents to synchronize a group of stepper motors. Modeling of the two-phase hybrid stepper motor in closed loop is derived in {d - q} rotary reference frame, based on field-oriented control techniques to provide torque control. The simulation obtained by MATLAB-Simulink shows that the distributed cooperative control effectiveness depends on the network topology defined by the graph.

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Dissertationen zum Thema "Cooperative distributed control"

Benahmed, Sif Eddine. "Distributed Cooperative Control for DC Microgrids." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0056.

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Au cours des dernières années, le réseau électrique connait une transformation rapide avec la pénétration massive des unités de production renouvelables et distribuées. Le concept de microgrids (micro-réseau électrique) est un élément clés de cette transition énergétique. Ces micro-réseaux sont constitués par un ensemble de plusieurs unités de production distribuées (DGUs), d'unités de stockage (SUs) et de charges interconnectées par des lignes électriques. Un microgrid peut être installé dans plusieurs endroits, par exemple dans des maisons, des hôpitaux, des quartiers, etc. et fonctionne soit en mode connecté au réseau principale, soit en mode isolé (autonome). Les microgrids sont confrontés à plusieurs défis liés à la garantie de la stabilité, la cybersécurité, l'optimisation des coûts énergétiques, la gestion de l'énergie, la qualité de l'énergie, etc. Dans ce travail, nous concentrons notre attention sur le contrôle des microgrids à courant continu en mode de fonctionnement autonome. La principale contribution de cette thèse est l’établissement de lois de commande par retour d’état distribuées assurant un partage de courant proportionnel entre les unités de production, une régulation de la tension moyenne des lignes et un équilibrage simultané des états de charge des éléments de stockage. En partant de l'hypothèse que les agents (DGU ou SU) ont les mêmes paramètres physiques, la preuve de la convergence exponentielle et globale est donnée en l’absence d’une connaissance de la charge présente sur le réseau. La thèse est divisée en trois parties. La première partie présente le concept des microgrids, un état de l’art sur leurs stratégies de contrôle et les préliminaires mathématiques nécessaires tout au long du manuscrit. La deuxième partie constitue la contribution théorique de cette thèse et aborde la synthèse de lois de contrôle distribuées, garantissant les objectifs envisagés en l’absence d’une connaissance de la charge variable sur le réseau et même en cas de perturbation constantes au niveau de l’entrée de commande. Cette garantie est apportée en considérant trois actions intégrales distribuées de type consensus. Dans la troisième partie, les contrôleurs proposés sont évalués dans différents scénarios par le biais de simulation Matlab/Simulink et de tests Hardware-in-the-Loop (HIL) en temps réel. Les résultats montrent que les objectifs de contrôle sont atteints avec succès, ce qui illustre l'efficacité de la méthodologie de contrôle proposée In recent years, the power grid has undergone a rapid transformation with the massive penetration of renewable and distributed generation units. The concept of microgrids is a key element of this energy transition. Microgrids are made up of a set of several distributed generation units (DGUs), storage units (SUs) and loads interconnected by power lines. A microgrid can be installed in several locations, for example in houses, hospitals, a neighborhood or village, etc., and operates either in connected mode to the main grid or in isolated (autonomous) mode. Microgrids are facing several challenges related to stability assurance, cyber-security, energy cost optimization, energy management, power quality, etc. In this work, we focus our attention on the control of islanded direct current microgrids. The main contribution is the design of a new distributed control approach to provably achieve current sharing, average voltage regulation and state-of-charge balancing simultaneously with global exponential convergence. The main tools are consensus in multi-agent systems, passivity, Lyapunov stability, linear matrix inequalities, etc. The thesis is divided into three parts. The First part presents the concept of microgrids, a literature review of their control strategies and the mathematical preliminaries required throughout the manuscript. The second part deals with the design of the proposed distributed control approach to achieve the considered objectives. The system is augmented with three distributed consensus-like integral actions, and a distributed-based static state feedback control architecture is proposed. Starting from the assumption that the agents (DGUs or SUs) have the same physical parameters, we provide proof of global exponential convergence. Moreover, the proposed control approach is distributed, i.e., each agent exchange relative information with only its neighbors through sparse communication networks. The proposed controllers do not need any information about the parameters of the power lines neither the topology of the microgrid. The control objectives are reached despite the unknown load variation and constant disturbances. In the third part, the proposed distributed controllers are assessed in different scenarios through Matlab/Simulink simulation and real-time Hardware-in-the-Loop experiment. The results show that the control objectives are successfully achieved, illustrating the effectiveness of the proposed control methodology

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Wen, Guoguang. "Distributed cooperative control for multi-agent systems." Phd thesis, Ecole Centrale de Lille, 2012. http://tel.archives-ouvertes.fr/tel-00818774.

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Cette thèse considère principalement trois problèmes dans le domaine du contrôle distribué coopératif des systèmes multi-agents(SMA): le consensus, la navigation en formation et le maintien en formation d'un groupe d'agents lorsqu'un agent disparait. Nous proposons 3 algorithmes pour résoudre le problème du calcul distribué d'un consensus à partir de l'approche leadeur-suiveur dans le contexte SMA à dynamique non-linéaire. La référence est définie comme un leader virtuel dont on n'obtient, localement, que les données de position et de vitesse. Pour résoudre le problème du suivi par consensus pour les SMA à dynamique non-linéaire, nous considérons le suivi par consensus pour SMA de premier ordre. On propose des résultats permettant aux suiveurs de suivre le leadeur virtuel en temps fini en ne considérant que les positions des agents. Ensuite, nous considérons le suivi par consensus de SMA de second. Dans le cas de la planification de trajectoire et la commande du mouvement de la formation multi-agents. L'idée est d'amener la formation, dont la dynamique est supposée être en 3D, d'une configuration initiale vers une configuration finale (trouver un chemin faisable en position et orientation) en maintenant sa forme tout le long du chemin en évitant les obstacles. La stratégie proposée se décompose en 3 étapes. Le problème du Closing-Rank se traduit par la réparation d'une formation rigide multi-agents "endommagée" par la perte de l'un de ses agents. Nous proposons 2 algorithmes d'autoréparation systématique pour récupérer la rigidité en cas de perte d'un agent. Ces réparations s'effectuent de manière décentralisée et distribuée n'utilisant que des informations de voisinage.

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Alabri, Said. "Distributed Extremum Seeking and Cooperative Control for Mobile Cooperative Communication Systems." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5596.

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In this thesis, a distributed extremum seeking and cooperative control algorithm is designed for mobile agents to disperse themselves optimally in maintaining communication quality and maximizing their coverage. The networked mobile agents locally form a virtual multiple-input multiple-output (MIMO) communication system, and they cooperatively communicate among them by using the decode and forward cooperative communication technique. The outage probability is used as the measure of communication quality, and it can be estimated real-time. A general performance index balancing outage probability and spatial dispersion is chosen for the overall system. The extremum seeking control approach is used to estimate and optimize the value of the performance index, and the cooperative formation control is applied to move the mobile agents to achieve the optimal solution by using only the locally-available information. Through the integration of cooperative communication and cooperative control, network connectivity and coverage of the mobile agents are much improved when compared to either non-cooperative communication approaches or other existing control results. Analytical analysis is carried out to demonstrate the performance and robustness of the proposal methodology, and simulation is done to illustrate its effectiveness. M.S.E.E. Masters Electrical Engineering and Computing Engineering and Computer Science Electrical Engineering

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Kreidl, O. Patrick. "Distributed cooperative control architectures for automated manufacturing systems." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11295.

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Feng, Jiao. "Distributed relay selection aided cooperative medium access control." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/362096/.

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A variety of cooperative medium access control (MAC) schemes are designed for the sake of improving the achievable transmit rate and for reducing the transmit energy dissipation of cooperative communication systems relying on realistic greedy - rather than altruistic - relay nodes (RNs). Based on the system’s objective functions (OF), novel distributed relay selection schemes are developed for selecting the best relay node (RN) set. In order to investigate the effect of the proposed MAC schemes on the performance of the cooperative communication systems considered, the system’s stability is analysed with the aid of queueing theory. Specifically, we first consider a cooperative spectrum leasing system (CSLS) supporting a licensed source node (SN) and a licensed destination node (DN) as well as multiple unlicensed greedy RNs, which require rewards for providing cooperative transmission assistance. A ’win-win’ (WW) cooperative framework (WWCF) is formulated for sake of improving the achievable transmit rate and for simulanteously minimizing the energy dissipation of the co- operative spectrum leasing system considered. Based on the proposed WWCF, the licensed SN intends to lease part of its spectrum to the unlicensed RNs in exchange for cooperative support, leading to an improved transmit rate, while simultaneously reducing the transmit power. The unlicensed RNs also have an incentive to provide cooperative transmission assistance for the SN, since in exchange for relaying assistance they are allowed to access the licensed spectrum for transmitting their own data, and even to maintain their own target Quality of Service (QoS). Furthermore, a distributed WW cooperative MAC protocol is developed for implementing the proposed WWCF by designing a specific signalling procedure and the format of both the data frame control messages as well as a distributed relay selection scheme. More explicitly, a novel backoff algorithm is designed for distributively selecting the best RN in order to optimize the system’s OF formulated by our WWCF. Our simulation results demonstrated that both substantial rate improvements and considerable energy savings are achieved by implementing the proposed distributed WW cooperative MAC protocol. However, encountering a low service rate at the MAC layer may excessively increase the length of queue in the buffer storing the incoming packet. Hence, the queueing system may become unstable due to the low service rate limited by an inferior MAC protocol design. Hence we conceived a queueing model for our cooperative spectrum leasing system relying on the proposed distributed WW cooperative MAC protocol. In order to simplify the stability analysis, some idealized simplifying assumptions are invoked and a non-Markovian analysis method is used for investigating the transmission probability of each node and for deriving the average departure rates at both the SN and the RNs operating under the control of the proposed distributed WW cooperative MAC protocol. Our simulation results confirmed that an increased stable throughput is provided by the proposed distributed WW cooperative MAC protocol for both the SN and RNs compared to the benchmark schemes. As an improved extension of the proposed WWCF, a WW reciprocal-selection-based framework (WWRSF) is formulated for a cooperative spectrum leasing system hosting multiple licensed transmission pairs and multiple unlicensed transmission pairs. The SN of a licensed pair of nodes is referred as the primary transmitter (PT), while the SN of an unlicensed transmission pair is termed as the secondary transmitter (ST). Based on the proposed WWRSF, the PT intends to lease its spectral resources to an appropriate secondary transmitter (ST) in exchange for cooperative transmission assistance for the sake of minimizing its transmit power and simultaneously satisfying its transmit rate requirement. The ST has an incentive to collaborate with the best PT for the sake of minimizing the ST’s transmit power under the constraint of its QoS requirement, whilst simultaneously winning a transmission opportunity for its own traffic. Based on the OFs of the proposed WWRSF, a distributed WW reciprocal-selection-based medium access scheme (DWWRS-MAS) is designed, which is capable of producing the best cooperative pairs set for the sake of reducing the transmit power of both the PT and of the ST in each cooperative pair, whilst simultaneously satisfying their transmit rate requirements. This is achieved with the aid of the proposed distributed reciprocal selection between the active PTs and STs, which have the capability of providing successful cooperative transmission assistance. Moreover, we analyse both the queueing stability and the algorithmic stability of our cooperative spectrum leasing system exploiting our DWWRS-MAS. In comparison to the benchmark schemes considered in the literature, the proposed DWWRS-MAS is capable of achieving a performance, which is comparable to that of the optimal schemes in terms of the system’s transmit power and system’s achievable transmit rate.

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Liu, Peng. "Distributed Model Predictive Control for Cooperative Highway Driving." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500564857136091.

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Jilg, Martin [Verfasser]. "Hierachical and Cooperative Control of Complex Distributed Systems / Martin Jilg." Kassel : Kassel University Press, 2018. http://d-nb.info/1153797011/34.

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Lim, Yusun. "Game theoretic distributed coordination: drifting environments and constrained communications." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52986.

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The major objective of this dissertation is extending the capabilities of game theoretic distributed control to more general settings. In particular, we are interested in drifting environments and/or constrained communications. The first part of the dissertation concerns slowly varying dynamics, i.e., drifting environments. A standard assumption in game theoretic learning is a stationary environment, e.g., the game is fixed. We investigate the case of slow variations and show that for sufficiently slow time variations, the limiting behavior “tracks” the stochastically stable states. Since the analysis is regarding Markov processes, the results could be applied to various game theoretic learning rules. In this research, the results were applied to log-linear learning. A mobile sensor coverage example was tested in both simulation and laboratory experiments. The second part considers a problem of coordinating team players' actions without any communications in team-based zero-sum games. Generally, some global signalling devices are required for common randomness between players, but communications are very limited or impossible in many practical applications. Instead of learning a one-shot strategy, we let players coordinate a periodic sequence of deterministic actions and put an assumption on opponent's rationality. Since team players' action sequences are periodic and deterministic, common randomness is no longer required to coordinate players. It is proved that if a length of a periodic action sequence is long enough, then opponents with limited rationality cannot recognize its pattern. Because the opponents cannot recognize that the players are playing deterministic actions, the players' behavior looks like a correlated and randomized joint strategy with empirical distribution of their action sequences. Consequently players can coordinate their action sequences without any communications or global signals, and the resulting action sequences have correlated behavior. Moreover, the notion of micro-players are introduced for efficient learning of long action sequences. Micro-player matching approach provides a new framework that converts the original team-based zero-sum game to a game between micro-players. By introducing a de Bruijn sequence to micro-player matching, we successfully separate the level of opponent's rationality and the size of the game of micro-players. The simulation results are shown to demonstrate the performance of micro-player matching methods. Lastly, the results of the previous two topics are combined by considering a problem of coordinating actions without communications in drifting environments. More specifically, it is assumed that the opponent player in the team-based zero-sum games tries to adjust its strategy in the set of bounded recall strategies. Then the time-varying opponent's strategy can be considered as a dynamic environment parameter in a coordination game between the team players. Additionally, we develop a human testbed program for further study regarding a human as an adaptive opponent in the team-based zero-sum games. The developed human testbed program can be a starting point for studying game theoretic correlated behavior learning against a human.

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Torabi, Zahra. "Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

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Maknouninejad, Ali. "Cooperative Control and Advanced Management of Distributed Generators in a Smart Grid." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5663.

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Smart grid is more than just the smart meters. The future smart grids are expected to include a high penetration of distributed generations (DGs), most of which will consist of renewable energy sources, such as solar or wind energy. It is believed that the high penetration of DGs will result in the reduction of power losses, voltage profile improvement, meeting future load demand, and optimizingthe use of non-conventionalenergy sources. However, more serious problems will arise if a decent control mechanism is not exploited. An improperly managed high PV penetration may cause voltage profile disturbance, conflict with conventional network protection devices, interfere with transformer tap changers, and as a result, cause network instability. Indeed, it is feasible to organize DGs in a microgrid structure which will be connected to the main grid through a point of common coupling (PCC). Microgrids are natural innovation zones for the smart grid because of their scalability and flexibility. A proper organization and control of the interaction between the microgrid and the smartgrid is a challenge. Cooperative control makes it possible to organize different agents in a networked system to act as a group and realize the designated objectives. Cooperative control has been already applied to the autonomous vehicles and this work investigates its application in controlling the DGs in a micro grid. The microgrid power objectives are set by a higher level control and the application of the cooperative control makes it possible for the DGs to utilize a low bandwidth communication network and realize the objectives. Initially, the basics of the application of the DGs cooperative control are formulated. This includes organizing all the DGs of a microgrid to satisfy an active and a reactive power objective. Then, the cooperative control is further developed by the introduction of clustering DGs into several groups to satisfy multiple power objectives. Then, the cooperative distribution optimization is introduced to optimally dispatch the reactive power of the DGs to realize a unified microgrid voltage profile and minimizethelosses. Thisdistributedoptimizationis agradient based techniqueand itis shown that when the communication is down, it reduces to a form of droop. However, this gradient based droop exhibits a superior performance in the transient response, by eliminating the overshoots caused by the conventional droop. Meanwhile, the interaction between each microgrid and the main grid can be formulated as a Stackelberg game. The main grid as the leader, by offering proper energy price to the micro grid, minimizes its cost and secures the power. This not only optimizes the economical interests of both sides, the microgrids and the main grid, but also yields an improved power flow and shaves the peak power. As such, a smartgrid may treat microgrids as individually dispatchable loads or generators. Ph.D. Doctorate Electrical Engineering and Computer Science Engineering and Computer Science Electrical Engineering

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Bücher zum Thema "Cooperative distributed control"

Guo, Yi. Distributed Cooperative Control. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119216131.

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Bidram, Ali, Vahidreza Nasirian, Ali Davoudi, and Frank L. Lewis. Cooperative Synchronization in Distributed Microgrid Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50808-5.

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S, Shamma Jeff, ed. Cooperative control of distributed multi-agent systems. John Wiley & Sons, 2007.

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S, Shamma Jeff, ed. Cooperative control of distributed multi-agent systems. John Wiley & Sons, 2007.

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Shamma, Jeff S., ed. Cooperative Control of Distributed Multi-Agent Systems. John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470724200.

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Yu, Wenwu, Guanghui Wen, Guanrong Chen, and Jinde Cao. Distributed Cooperative Control of Multi-agent Systems. John Wiley & Sons Singapore Pte. Ltd, 2016. http://dx.doi.org/10.1002/9781119246213.

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Ren, Wei, and Randal W. Beard. Distributed Consensus in Multi-vehicle Cooperative Control. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-015-5.

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Zou, Yuanyuan, and Shaoyuan Li. Distributed Cooperative Model Predictive Control of Networked Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6084-0.

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Yue, Dong, Huaipin Zhang, and Shengxuan Weng. Distributed Cooperative Control and Communication for Multi-agent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6718-0.

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W, Beard Randal, ed. Distributed consensus in multi-vehicle cooperative control: Theory and applications. Springer, 2008.

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Buchteile zum Thema "Cooperative distributed control"

Bullo, Francesco, and Jorge Cortés. "Adaptive and Distributed Coordination Algorithms for Mobile Sensing Networks." In Cooperative Control. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-31595-7_3.

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Ganapathy, Sriram, and Kevin M. Passino. "Distributed Agreement Strategies for Cooperative Control." In Cooperative Systems. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4613-0219-3_9.

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Li, Shaoyuan, Yi Zheng, and Binqiang Xue. "Cooperative Distributed Predictive Control System." In Intelligent Optimal Control for Distributed Industrial Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0268-2_6.

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Shamma, Jeff S., and Gurdal Arslan. "Dimensions of Cooperative Control." In Cooperative Control of Distributed Multi-Agent Systems. John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470724200.ch1.

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Bidram, Ali, Vahidreza Nasirian, Ali Davoudi, and Frank L. Lewis. "Cooperative Control for DC Microgrids." In Cooperative Synchronization in Distributed Microgrid Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50808-5_7.

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Bidram, Ali, Vahidreza Nasirian, Ali Davoudi, and Frank L. Lewis. "Distributed Control of AC Microgrids." In Cooperative Synchronization in Distributed Microgrid Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50808-5_4.

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Cai, He, Youfeng Su, and Jie Huang. "The Distributed Observer Approach." In Cooperative Control of Multi-agent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98377-2_4.

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Zwicker, Eckart, and Claus Rottenbacher. "Distributed Cooperative Budget-Planning and -Control." In Distributed Information Systems in Business. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80216-4_5.

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Mohanty, Nishant, Jaskaran Grover, Changliu Liu, and Katia Sycara. "Distributed Multirobot Control for Non-cooperative Herding." In Distributed Autonomous Robotic Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51497-5_23.

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Bidram, Ali, Vahidreza Nasirian, Ali Davoudi, and Frank L. Lewis. "Control and Modeling of Microgrids." In Cooperative Synchronization in Distributed Microgrid Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50808-5_2.

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Konferenzberichte zum Thema "Cooperative distributed control"

Lihua, Xie. "Distributed cooperative control and optimization." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064479.

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Tomic, Ivana, Efstathios Milonidis, and George D. Halikias. "LQR distributed cooperative control of a formation of low-speed experimental UAVs." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737541.

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Jones, Patricia M. "Cooperative support for distributed supervisory control." In the 1st international conference. ACM Press, 1993. http://dx.doi.org/10.1145/169891.170008.

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Stewart, B. T., J. B. Rawlings, and S. J. Wright. "Hierarchical cooperative distributed model predictive control." In 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530634.

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Burger, Mathias, Giuseppe Notarstefano, and Frank Allgower. "From non-cooperative to cooperative distributed MPC: A simplicial approximation perspective." In 2013 European Control Conference (ECC). IEEE, 2013. http://dx.doi.org/10.23919/ecc.2013.6669691.

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Zhao, Qilun, Xiwang Dong, Chen Jian, Chen Bai, Qingdong Li, and Zhang Ren. "Distributed cooperative guidance for multiple missiles." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554187.

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Renzaglia, Alessandro, Lefteris Doitsidis, Agostino Martinelli, and Elias B. Kosmatopoulos. "Adaptive-based distributed cooperative multi-robot coverage." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990822.

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Wei, Liu, Xu Xiaoming, and Zhang Zhongjun. "Distributed Cooperative Scheduling for a Job Shop." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792195.

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Chatzipanagiotis, Nikolaos, Athina Petropulu, and Michael M. Zavlanos. "A distributed algorithm for cooperative relay beamforming." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580418.

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Verginis, Christos K., and Dimos V. Dimarogonas. "Distributed cooperative manipulation under timed temporal specifications." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963141.

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Berichte der Organisationen zum Thema "Cooperative distributed control"

Feddema, John Todd, Eric Paul Parker, John S. Wagner, and David Alan Schoenwald. Analysis and control of distributed cooperative systems. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/919643.

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Hurtado, John E. Distributed Sensing & Cooperative Control for Plume Tracing. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada410645.

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Parker, L. E. Distributed Planning and Control for Teams of Cooperating Mobile Robots. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/940244.

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Basar, Tamer. Distributed Control for Networked Systems with Non-Traditional Communication Constraints: Lossy Links, Power and Usage Limitations, and Induced Cooperation. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada576967.

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Kwon, Heeseo Rain, HeeAh Cho, Jongbok Kim, Sang Keon Lee, and Donju Lee. International Case Studies of Smart Cities: Anyang, Republic of Korea. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007013.

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This case study is one of ten international studies developed by the Korea Research Institute for Human Settlements (KRIHS), in association with the Inter-American Development Bank (IDB), for the cities of Anyang, Medellin, Namyangju, Orlando, Pangyo, Rio de Janeiro, Santander, Singapore, Songdo, and Tel Aviv. At the IDB, the Competitiveness and Innovation Division (CTI), the Fiscal and Municipal Management Division (FMM), and the Emerging and Sustainable Cities Initiative (ESCI) coordinated the study. This project was part of technical cooperation ME-T1254, financed by the Knowledge Partnership Korean Fund for Technology and Innovation of the Republic of Korea. At KRIHS, the National Infrastructure Research Division coordinated the project and the Global Development Partnership Center provided the funding. Anyang, a 600,000 population city near Seoul is developing international recognition on its smart city project that has been implemented incrementally since 2003. This initiative began with the Bus Information System to enhance citizen's convenience at first, and has been expanding its domain into wider Intelligent Transport System as well as crime and disaster prevention in an integrated manner. Anyang is evaluated as a benchmark for smart city with a 2012 Presidential Award in Korea and receives large number of international visits. Anyang's Integrated Operation and Control Center (IOCC) acts as the platform that gathers, analyzes and distributes information for mobility, disasters management and crime. Anyang is currently utilizing big data for policy development and is continuing its endeavor to expand its smart city services into areas such as waste and air quality management. Anyang's success factors are the government officials' continuous willingness towards service development and the establishment of cooperation system among the smart city-related organizations.

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Colomb, Claire, and Tatiana Moreira de Souza. Regulating Short-Term Rentals: Platform-based property rentals in European cities: the policy debates. Property Research Trust, 2021. http://dx.doi.org/10.52915/kkkd3578.

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Short-term rentals mediated by digital platforms have positive and negative impacts that are unevenly distributed among socio-economic groups and places. Detrimental impacts on the housing market and quality of life of long-term residents have been particular contentious in some cities. • In the 12 cities studied in the report (Amsterdam, Barcelona, Berlin, Brussels, Lisbon, London, Madrid, Milan, Paris, Prague, Rome and Vienna), city governments have responded differently to the growth of short-term rentals. • The emerging local regulations of short-term rentals take multiple forms and exhibit various degrees of stringency, ranging from rare cases of laissez-faire to a few cases of partial prohibition or strict quantitative control. Most city governments have sought to find a middle-ground approach that differentiates between the professional rental of whole units and the occasional rental of one’s home/ primary residence. • The regulation of short-term rentals is contentious and highly politicised. Six broad categories of interest groups and non-state actors actively participate in the debates with contrasting positions: advocates of the ‘sharing’ or ‘collaborative’ economy; corporate platforms; professional organisatons of short-term rental operators; new associations of hosts or ‘home-sharers’; the hotel and hospitality industry; and residents’ associations/citizens’ movements. • All city governments face difficulties in implementing and enforcing the regulations, due to a lack of sufficient resources and to the absence of accurate and comprehensive data on individual hosts. That data is held by corporate platforms, which have generally not accepted to release it (with a few exceptions) nor to monitor the content of their listings against local rules. • The relationships between platforms and city governments have oscillated between collaboration and conflict. Effective implementation is impossible without the cooperation of platforms. • In the context of the European Union, the debate has taken a supranational dimension, as two pieces of EU law frame the possibility — and acceptable forms — of regulation of online platforms and of short-term rentals in EU member states: the 2000 E-Commerce Directive and the 2006 Services Directive. • For regulation to be effective, the EU legal framework should be revised to ensure platform account- ability and data disclosure. This would allow city (and other ti ers of) governments to effectively enforce the regulations that they deem appropriate. • Besides, national and regional governments, who often control the legislative framework that defines particular types of short-term rentals, need to give local governments the necessary tools to be able to exercise their ‘right to regulate’ in the name of public interest objectives.

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