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Artículos de revistas sobre el tema "Cooperative distributed control"

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Publicado: 25 de mayo de 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

HARAMAKI, Shinya, Akihiro HAYASHI, Toshifumi SATAKE, and Shigeru AOMURA. "Distributed Cooperative Control System for Multi-jointed Redundant Manipulator(Control Theory and Application,Session: MA1-B)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 21. http://dx.doi.org/10.1299/jsmeicam.2004.4.21_2.

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12

Liu, Xiangdong, Haikuo Liu, Changkun Du, Pingli Lu, Dongping Jin, and Fushou Liu. "Distributed active vibration cooperative control for flexible structure with multiple autonomous substructure model." Journal of Vibration and Control 26, no. 21-22 (2020): 2026–36. http://dx.doi.org/10.1177/1077546320909968.

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The objective of this work was to suppress the vibration of flexible structures by using a distributed cooperative control scheme with decentralized sensors and actuators. For the application of the distributed cooperative control strategy, we first propose the multiple autonomous substructure models for flexible structures. Each autonomous substructure is equipped with its own sensor, actuator, and controller, and they all have computation and communication capabilities. The primary focus of this investigation was to illustrate the use of a distributed cooperative protocol to enable vibration control. Based on the proposed models, we design two novel active vibration control strategies, both of which are implemented in a distributed manner under a communication network. The distributed controllers can effectively suppress the vibration of flexible structures, and a certain degree of interaction cooperation will improve the performance of the vibration suppression. The stability of flexible systems is analyzed by the Lyapunov theory. Finally, numerical examples of a cantilever beam structure demonstrate the effectiveness of the proposed methods.
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13

Yasuda, Gen'ichi. "Modular Distributed Control Architecture for Cooperative Soccer-Playing Robot Agents." Journal of Robotics and Mechatronics 12, no. 1 (2000): 29–34. http://dx.doi.org/10.20965/jrm.2000.p0029.

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We describe the concept and implementation of modular distributed control architecture for cooperative soccer-playing robot agents. Overall complete autonomous control for robotic soccer consists of the host and several onboard control systems. Onboard control for an autonomous mobile robot with intelligent sensors and actuators is constructed on microcontrollerbased flexible, extendable architecture whose microcontrollers are dedicated to low-level control for navigation based on multiaxis and multisensor cooperation. Operations of autonomous actuators are integrated through a serial-bus communication network. Distributed implementation reduces difficulties in complex hardware and software design of the control system. We evaluated basic control executed on microcontrollers. The host conducts high-level decision-making and cooperative action planning for robot agents. The implementation of basic skills and strategies for robotic soccer is discussed.
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14

Lerner, Vladimir S. "Cooperative information space distributed macromodels." International Journal of Control 81, no. 5 (2008): 725–51. http://dx.doi.org/10.1080/00207170701248439.

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15

Ferramosca, A., D. Limon, I. Alvarado, and E. F. Camacho. "Cooperative distributed MPC for tracking." Automatica 49, no. 4 (2013): 906–14. http://dx.doi.org/10.1016/j.automatica.2013.01.019.

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16

NAGAO, Yoichi, Hideaki OHTA, Hironobu URABE, Shin-ichi NAKANO, and Sadatoshi KUMAGAI. "Net-Based Cooperative Control for Autonomous Distributed Systems." Transactions of the Society of Instrument and Control Engineers 32, no. 6 (1996): 967–74. http://dx.doi.org/10.9746/sicetr1965.32.967.

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17

Marino, Alessandro. "Distributed Adaptive Control of Networked Cooperative Mobile Manipulators." IEEE Transactions on Control Systems Technology 26, no. 5 (2018): 1646–60. http://dx.doi.org/10.1109/tcst.2017.2720673.

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18

Pischella, Mylene, and Jean-claude Belfiore. "Power control in distributed cooperative OFDMA cellular networks." IEEE Transactions on Wireless Communications 7, no. 5 (2008): 1900–1906. http://dx.doi.org/10.1109/twc.2008.061039.

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19

Stewart, Brett T., Stephen J. Wright, and James B. Rawlings. "Cooperative distributed model predictive control for nonlinear systems." Journal of Process Control 21, no. 5 (2011): 698–704. http://dx.doi.org/10.1016/j.jprocont.2010.11.004.

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20

Wang, Gang, Chaoli Wang, Qinghui Du, Lin Li, and Wenjie Dong. "Distributed Cooperative Control of Multiple Nonholonomic Mobile Robots." Journal of Intelligent & Robotic Systems 83, no. 3-4 (2016): 525–41. http://dx.doi.org/10.1007/s10846-015-0316-x.

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21

Müller, Matthias A., Marcus Reble, and Frank Allgöwer. "A general distributed MPC framework for cooperative control." IFAC Proceedings Volumes 44, no. 1 (2011): 7987–92. http://dx.doi.org/10.3182/20110828-6-it-1002.02884.

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22

Wang, Jianhong, Jorge De J. Lozoya Santos, and Ricardo A. Ramirez Mendoza. "Stability analysis in cooperative distributed model predictive control." International Journal of System of Systems Engineering 9, no. 4 (2019): 371. http://dx.doi.org/10.1504/ijsse.2019.10025801.

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23

Wang, Jianhong, Ricardo A. Ramirez Mendoza, and Jorge De J. Lozoya Santos. "Stability analysis in cooperative distributed model predictive control." International Journal of System of Systems Engineering 9, no. 4 (2019): 371. http://dx.doi.org/10.1504/ijsse.2019.104187.

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24

Li, Yue, Jun Yang, and Ke Zhang. "Distributed Finite-Time Cooperative Control for Quadrotor Formation." IEEE Access 7 (2019): 66753–63. http://dx.doi.org/10.1109/access.2019.2915594.

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25

Hao, Shenxue, Licai Yang, Li Ding, and Yajuan Guo. "Distributed Cooperative Backpressure-Based Traffic Light Control Method." Journal of Advanced Transportation 2019 (March 5, 2019): 1–14. http://dx.doi.org/10.1155/2019/7481489.

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On the foundation of the original backpressure-based traffic light control algorithm, a distributed cooperative backpressure-based traffic light control method is proposed in this paper. The urban traffic network is modeled as a smart agent-controlled queuing network, in which the intersection agents exchange the queue length information and the selected activating light phase information of neighboring intersections through communications and determine the activating light phase at each time slot according to local traffic information. The improved phase pressure computation method considers the phase state of downstream intersections instead of only the queue length of the local intersections. Light phase switching coordination among adjacent intersections is achieved using the consensus-based bundle algorithm, in which the cooperative light phase switching problem is viewed as a task assignment issue among adjacent intersections. Simulation results illustrated that the proposed cooperative backpressure-based traffic light control method obtained better performance than the original backpressure-based and fixed-time traffic control methods.
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26

Blasi, Svenja, Markus Kögel, and Rolf Findeisen. "Distributed Model Predictive Control Using Cooperative Contract Options." IFAC-PapersOnLine 51, no. 20 (2018): 448–54. http://dx.doi.org/10.1016/j.ifacol.2018.11.048.

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27

Spudić, V., C. Conte, M. Baotić, and M. Morari. "Cooperative distributed model predictive control for wind farms." Optimal Control Applications and Methods 36, no. 3 (2014): 333–52. http://dx.doi.org/10.1002/oca.2136.

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28

Hu, Haimin, Konstantinos Gatsis, Manfred Morari, and George J. Pappas. "Non-Cooperative Distributed MPC with Iterative Learning." IFAC-PapersOnLine 53, no. 2 (2020): 5225–32. http://dx.doi.org/10.1016/j.ifacol.2020.12.1198.

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29

Carnevale, Guido, Nicola Mimmo, and Giuseppe Notarstefano. "Aggregative feedback optimization for distributed cooperative robotics." IFAC-PapersOnLine 55, no. 13 (2022): 7–12. http://dx.doi.org/10.1016/j.ifacol.2022.07.227.

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30

Vainio, M., P. Appelqvist, and A. Halme. "Mobile robot society for distributed operations in closed aquatic environment." Robotica 18, no. 3 (2000): 235–50. http://dx.doi.org/10.1017/s0263574799002222.

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In this paper a multirobot system consisting of small size ball-shaped mobile underwater robots is introduced. Robots form a cooperative society operating together for a common goal. This is made possible by inter-member communication and control architecture allowing cooperation. The test environment is a closed aquatic process containing tanks, pipes, and a jet pump. The task considered is cleaning of biologically contaminated spots in the process. Detailed hardware structure of a robot-member as well as the control architecture are introduced. Behaviour of the cooperative system is demonstrated in a test environment where contamination caused by biological algae growth is emulated by infrared panels behaving like a living biomass.
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31

Vega, Carlos J., Larbi Djilali, and Edgar N. Sanchez. "Secondary Control of Microgrids via Neural Inverse Optimal Distributed Cooperative Control." IFAC-PapersOnLine 53, no. 2 (2020): 7891–96. http://dx.doi.org/10.1016/j.ifacol.2020.12.1973.

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32

Wang, Yinqiu, Qinghe Wu, and Yao Wang. "Distributed cooperative control for multiple quadrotor systems via dynamic surface control." Nonlinear Dynamics 75, no. 3 (2013): 513–27. http://dx.doi.org/10.1007/s11071-013-1081-7.

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33

Müller, Matthias A., Marcus Reble, and Frank Allgöwer. "Cooperative control of dynamically decoupled systems via distributed model predictive control." International Journal of Robust and Nonlinear Control 22, no. 12 (2012): 1376–97. http://dx.doi.org/10.1002/rnc.2826.

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34

Binfet, Philipp, Janis Adamek, Nils Schlüter, and Moritz Schulze Darup. "Towards privacy-preserving cooperative control via encrypted distributed optimization." at - Automatisierungstechnik 71, no. 9 (2023): 736–47. http://dx.doi.org/10.1515/auto-2023-0082.

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Abstract Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data are sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing approaches mainly focus on cloud-based control and distributed schemes are restrictive. In this paper, we present a novel privacy-preserving cooperative control scheme based on encrypted distributed optimization. More precisely, we focus on a secure distributed solution of a general consensus problem, which has manifold applications in cooperative control, by means of the alternating direction method of multipliers (ADMM). As a unique feature of our approach, we explicitly take into account the common situation that local decision variables contain copies of quantities associated with neighboring agents and ensure the neighbor’s privacy. We show the effectiveness of our method based on a numerical case study dealing with the formation of mobile robots.
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35

Li, Xue, Zhikang Fan, Shengfeng Wang, Aibing Qiu, and Jingfeng Mao. "A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems." Sensors 22, no. 7 (2022): 2480. http://dx.doi.org/10.3390/s22072480.

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This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework.
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36

Allman, Andrew, and Qi Zhang. "Distributed cooperative industrial demand response." Journal of Process Control 86 (February 2020): 81–93. http://dx.doi.org/10.1016/j.jprocont.2019.12.011.

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37

Xu, Chuang, Baolin Wu, and Yingchun Zhang. "Distributed prescribed-time attitude cooperative control for multiple spacecraft." Aerospace Science and Technology 113 (June 2021): 106699. http://dx.doi.org/10.1016/j.ast.2021.106699.

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38

Aluko, Anuoluwapo, Elutunji Buraimoh, Oluwafemi Emmanuel Oni, and Innocent Ewean Davidson. "Advanced Distributed Cooperative Secondary Control of Islanded DC Microgrids." Energies 15, no. 11 (2022): 3988. http://dx.doi.org/10.3390/en15113988.

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In an islanded DC microgrid with multiple distributed generators (DGs), the droop control is employed to realize proportional current sharing among the DGs in the microgrid. The action of the droop control causes a deviation in the DC bus voltage which is exacerbated by the line impedance between the DG and the DC bus. In this paper, an advanced distributed secondary control scheme is proposed to simultaneously achieve accurate voltage regulation and cooperative current sharing in the islanded DC microgrid system. The proposed distributed secondary controller is introduced in the cyber layer of the system, and each controller shares information with neighbouring controllers via a communication network. The distributed technique maintains the reliability of the overall system if some part of the communication link fails. The proposed controller uses the type-II fuzzy logic scheme to adaptively select the secondary control parameters for an improved response of the controller. The sufficient conditions to guarantee the stability of the proposed controller are derived using the Lyapunov method. Comprehensive tests under different operating scenarios are conducted to demonstrate the robustness of the proposed control scheme.
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39

Maestre, J. M., D. Muñoz de la Peña, A. Jiménez Losada, E. Algaba Durán, and E. F. Camacho. "An application of Cooperative Game Theory to Distributed Control." IFAC Proceedings Volumes 44, no. 1 (2011): 9121–26. http://dx.doi.org/10.3182/20110828-6-it-1002.00682.

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40

Serag, H. M. "Distributed control for cooperative systems governed by Schrodinger operator." Journal of Discrete Mathematical Sciences and Cryptography 3, no. 1-3 (2000): 227–34. http://dx.doi.org/10.1080/09720529.2000.10697910.

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41

Kodama, Junichi, Hiroshi Shinji, Takayuki Tanabe, Tomoki Hamagami, and Hironori Hirata. "Cooperative Control for Distributed Generation by using Multiagent Learning." IEEJ Transactions on Electronics, Information and Systems 126, no. 2 (2006): 194–95. http://dx.doi.org/10.1541/ieejeiss.126.194.

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42

Huang, Sunan, Rodney Swee Huat Teo, and Wenqi Liu. "Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles." Actuators 8, no. 1 (2018): 1. http://dx.doi.org/10.3390/act8010001.

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It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method.
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43

Lai, Jingang, Xiaoqing Lu, Xinghuo Yu, and Antonello Monti. "Cluster-Oriented Distributed Cooperative Control for Multiple AC Microgrids." IEEE Transactions on Industrial Informatics 15, no. 11 (2019): 5906–18. http://dx.doi.org/10.1109/tii.2019.2908666.

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44

Dohmann, Pablo Budde gen, and Sandra Hirche. "Distributed Control for Cooperative Manipulation With Event-Triggered Communication." IEEE Transactions on Robotics 36, no. 4 (2020): 1038–52. http://dx.doi.org/10.1109/tro.2020.2973096.

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45

Wu, Xiangyu, Chen Shen, and Reza Iravani. "A Distributed, Cooperative Frequency and Voltage Control for Microgrids." IEEE Transactions on Smart Grid 9, no. 4 (2018): 2764–76. http://dx.doi.org/10.1109/tsg.2016.2619486.

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46

Meng, Deyuan. "Dynamic Distributed Control for Networks With Cooperative–Antagonistic Interactions." IEEE Transactions on Automatic Control 63, no. 8 (2018): 2311–26. http://dx.doi.org/10.1109/tac.2017.2763536.

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47

Klavins, E., and R. M. Murray. "Sensor and actuator networks - Distributed algorithms for cooperative control." IEEE Pervasive Computing 3, no. 1 (2004): 56–65. http://dx.doi.org/10.1109/mprv.2004.1269132.

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48

Bidram, Ali, Ali Davoudi, Frank L. Lewis, and Josep M. Guerrero. "Distributed Cooperative Secondary Control of Microgrids Using Feedback Linearization." IEEE Transactions on Power Systems 28, no. 3 (2013): 3462–70. http://dx.doi.org/10.1109/tpwrs.2013.2247071.

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49

Li, Le, Rudy R. Negenborn, and Bart De Schutter. "Distributed model predictive control for cooperative synchromodal freight transport." Transportation Research Part E: Logistics and Transportation Review 105 (September 2017): 240–60. http://dx.doi.org/10.1016/j.tre.2016.08.006.

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50

Wang, Jianan, and Ming Xin. "Distributed optimal cooperative tracking control of multiple autonomous robots." Robotics and Autonomous Systems 60, no. 4 (2012): 572–83. http://dx.doi.org/10.1016/j.robot.2011.12.002.

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