Relevant bibliographies by topics / Control systems

Academic literature on the topic 'Control systems'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Control systems"

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Pandolfi, L. "Generalized control systems, boundary control systems, and delayed control systems." Mathematics of Control, Signals, and Systems 3, no. 2 (June 1990): 165–81. http://dx.doi.org/10.1007/bf02551366.

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Irfan, C. M. Althaff, Karim Ouzzane, Shusaku Nomura, and Yoshimi Fukumura. "211 AN ACCESS CONTROL SYSTEM For E-Learning MANAGEMENT SYSTEMS." Proceedings of Conference of Hokuriku-Shinetsu Branch 2010.47 (2010): 59–60. http://dx.doi.org/10.1299/jsmehs.2010.47.59.

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Trofimov, A. M., and V. M. Moskovkin. "Optimal control over geomorphological systems." Zeitschrift für Geomorphologie 29, no. 3 (October 31, 1985): 257–63. http://dx.doi.org/10.1127/zfg/29/1985/257.

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Augusto Arbugeri, Cesar, Neilor Colombo Dal Pont, Tiago Kommers Jappe, Samir Ahmad Mussa, and Telles Brunelli Lazzarin. "Control System for Multi-Inverter Parallel Operation in Uninterruptible Power Systems." Eletrônica de Potência 24, no. 1 (February 1, 2018): 37–46. http://dx.doi.org/10.18618/rep.2019.1.0016.

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Otto, Samuel E., and Clarence W. Rowley. "Koopman Operators for Estimation and Control of Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 59–87. http://dx.doi.org/10.1146/annurev-control-071020-010108.

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A common way to represent a system's dynamics is to specify how the state evolves in time. An alternative viewpoint is to specify how functions of the state evolve in time. This evolution of functions is governed by a linear operator called the Koopman operator, whose spectral properties reveal intrinsic features of a system. For instance, its eigenfunctions determine coordinates in which the dynamics evolve linearly. This review discusses the theoretical foundations of Koopman operator methods, as well as numerical methods developed over the past two decades to approximate the Koopman operator from data, for systems both with and without actuation. We pay special attention to ergodic systems, for which especially effective numerical methods are available. For nonlinear systems with an affine control input, the Koopman formalism leads naturally to systems that are bilinear in the state and the input, and this structure can be leveraged for the design of controllers and estimators.
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Han, Shuo, and George J. Pappas. "Privacy in Control and Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 309–32. http://dx.doi.org/10.1146/annurev-control-060117-105018.

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Many modern dynamical systems, such as smart grids and traffic networks, rely on user data for efficient operation. These data often contain sensitive information that the participating users do not wish to reveal to the public. One major challenge is to protect the privacy of participating users when utilizing user data. Over the past decade, differential privacy has emerged as a mathematically rigorous approach that provides strong privacy guarantees. In particular, differential privacy has several useful properties, including resistance to both postprocessing and the use of side information by adversaries. Although differential privacy was first proposed for static-database applications, this review focuses on its use in the context of control systems, in which the data under processing often take the form of data streams. Through two major applications—filtering and optimization algorithms—we illustrate the use of mathematical tools from control and optimization to convert a nonprivate algorithm to its private counterpart. These tools also enable us to quantify the trade-offs between privacy and system performance.
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Goncharenko, Borys, Larysa Vikhrova, and Mariia Miroshnichenko. "Optimal control of nonlinear stationary systems at infinite control time." Central Ukrainian Scientific Bulletin. Technical Sciences, no. 4(35) (2021): 88–93. http://dx.doi.org/10.32515/2664-262x.2021.4(35).88-93.

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The article presents a solution to the problem of control synthesis for dynamical systems described by linear differential equations that function in accordance with the integral-quadratic quality criterion under uncertainty. External perturbations, errors and initial conditions belong to a certain set of uncertainties. Therefore, the problem of finding the optimal control in the form of feedback on the output of the object is presented in the form of a minimum problem of optimal control under uncertainty. The problem of finding the optimal control and initial state, which maximizes the quality criterion, is considered in the framework of the optimization problem, which is solved by the method of Lagrange multipliers after the introduction of the auxiliary scalar function - Hamiltonian. The case of a stationary system on an infinite period of time is considered. The formulas that can be used for calculations are given for the first and second variations. It is proposed to solve the problem of control search in two stages: search of intermediate solution at fixed values of control and error vectors and subsequent search of final optimal control. The solution of -optimal control for infinite time taking into account the signal from the compensator output is also considered, as well as the solution of the corresponding matrix algebraic equations of Ricatti type.
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Zhang, Haitao, and Zhen Li. "Fuzzy Immune Control Based Smith Predictor for Networked Control Systems." International Journal of Engineering and Technology 3, no. 1 (2011): 81–84. http://dx.doi.org/10.7763/ijet.2011.v3.204.

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Tašner, Tadej, and Darko Lovrec. "Maximum Efficiency Control – A New Strategy To Control Electrohydraulic Systems." Paripex - Indian Journal Of Research 3, no. 5 (January 15, 2012): 107–9. http://dx.doi.org/10.15373/22501991/may2014/34.

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ME, E. Sankaran. "Distributed Control Systems in Food Processing." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 27–30. http://dx.doi.org/10.31142/ijtsrd18921.

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Dissertations / Theses on the topic "Control systems"

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Conocimiento, Dirección de Gestión del. "IEEE Control Systems." IEEE, 2004. http://hdl.handle.net/10757/655310.

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Medina, Enrique A. "Linear Impulsive Control Systems: A Geometric Approach." Ohio : Ohio University, 2007. http://www.ohiolink.edu/etd/view.cgi?ohiou1187704023.

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Zhao, Yun-Bo. "Packet-Based Control for Networked Control Systems." Thesis, University of South Wales, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490204.

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Networked control systems (NCSs) are such control systems where the control loop is closed via some form of communication networks. These control systems are widely applicable in remote and distributed control applications. The inserted network however presents great challenges to conventional control theory as far as the design and analysis of NCSs are concerned. These challenges are caused primarily by the communication constraints in NCSs, e.g., network-induced delay, data packet dropout, data packet disorder, network access constraint, etc., which significantly degrade the system performance or even destabilize the system. When applying conventional control approaches to NCSs, considerable conservativeness is inevitable due to the failure to exploit network characteristics. Therefore, the co-design approach to NCSs in which control approaches and characteristics of NCSs are both fully considered, is believed to be the best way forward for the design of NCSs. In this thesis, we investigate the packet-based transmission of the network being used in NCSs, and propose a packet-based control (PB-control) approach to NCSs. In this approach, the 'packet' structure of data transmission in NCSs which is distinct from conventional control systems, is taken advantage of where, the control signals are first 'packed' and then sent as a sequence instead of one at a time as done in conventional control systems. \Vith the efficient use of the 'packet' structure, we can then actively compensate for the communication constraints in NCSs including the network-induced delay, data packet dropout and data packet disorder simultaneously. After determining the PB-control structure, we then extend its application to several categories of problems as follows. j • The first application is to two types of special nonlinear systems described by a Hammerstein model and a Wiener model respectively. A 'two-step' approach is adopted in this situation to separate the nonlinear process from the whole system which then enables the PB-control approach to be implemented. • It is observed that the communication constraints in NeSs are stochastic in nature, and thus a stochastic analysis of the PB-control approach is presented -----'''-'--'--~-• .:.o'... '-~.::C''c:....'..:..'..;...';';;;'~~.~'----' ......;.''''- ---'- ..-..;.;.~~ / iii under the Markov jump system framework, by modeling the network-induced delay and data packet dropout as a homogeneous ergodic Markov chain. The sufficient and necessary conditions for stochastic stability and stabilization in this situation are also obtained. • Continuous-time plant and continuous network-induced delay are observed to be more difficult to handle when implementing the PB-control approach. For this challenge, a discretization technique is introduced for the continuous network-induced delay and as a result, a novel model for NCSs is derived which is different to that obtained by conventional analysis from time delay system theory. A stabilized controller is also obtained in this situation by using delay-dependent analysis. • The last application is to deal with the situation where a set of NeSs share the network and thus the network access constraint has to be considered. For this situation, a PB-control and scheduling co-design approach is proposed where, PB-control is still applied to each subsystem while scheduling algorithms are applied to schedule the network resources among the subsystems to guarantee the stability of the whole system. We also point out in the thesis that further research on the PB-control approach is still needed as far as nonlinear, continuous-time systems and stochastic analysis are concerned.
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Scott, Wesley Dane. "A flexible control system for flexible manufacturing systems." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/158.

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A flexible workcell controller has been developed using a three level control hierarchy (workcell, workstation, equipment). The cell controller is automatically generated from a model input by the user. The model consists of three sets of graphs. One set of graphs describes the process plans of the parts produced by the manufacturing system, one set describes movements into, out of and within workstations, and the third set describes movements of parts/transporters between workstations. The controller uses an event driven Petri net to maintain state information and to communicate with lower level controllers. The control logic is contained in an artificial neural network. The Petri net state information is used as the input to the neural net and messages that are Petri net events are output from the neural net. A genetic algorithm was used to search over alternative operation choices to find a "good" solution. The system was fully implemented and several test cases are described.
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Corradino, Claudia. "Hybrid System of Systems Control : the TOKAMAK scenario." Doctoral thesis, Università di Catania, 2018. http://hdl.handle.net/10761/3820.

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The basic definition of System-of-Systems (SoS) is that of a large-scale integration of many independent, self-contained systems having the common aim of satisfying a global need. Under this perspective, lots of systems of systems can be found in several fields where a common final goal drives systems towards a final SoS state. In nuclear fusion research area, several examples of SoS applications can be made. From the integration of all the constituents of the TOKAMAK machines which work together to achieve a sustained nuclear fusion reaction, to the circuits made of active analogue components mimicking plasma behavior, to the neural networks made of connected units working together in order to predict plasma variables behavior. In this work, investigation of several SoS relevant in TOKAMAK scenario is performed and interesting results enhancing the plant capabilities provided. This thesis itself has been structured with a SoS-like structures where the integration of self-contained chapters is performed in order to satisfy the global goal that is improving TOKAMAK performances.
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Ouyang, Hua. "Networked predictive control systems : control scheme and robust stability." Thesis, University of South Wales, 2007. https://pure.southwales.ac.uk/en/studentthesis/networked-predictive-control-systems(9c6178d7-e6a4-420b-b35f-2d62d35ff5b0).html.

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Networked predictive control is a new research method for Networked Control Systems (NCS), which is able to handle network-induced problems such as time-delay, data dropouts, packets disorders, etc. while stabilizing the closed-loop system. This work is an extension and complement of networked predictive control methodology. There is always present model uncertainties or physical nonlinearity in the process of NCS. Therefore, it makes the study of the robust control of NCS and that of networked nonlinear control system (NNCS) considerably important. This work studied the following three problems: the robust control of networked predictive linear control systems, the control scheme for networked nonlinear control systems (NNCS) and the robust control of NNCS. The emphasis is on stability analysis and the design of robust control. This work adapted the two control schemes, namely, the time-driven and the event driven predictive controller for the implementation of NCS. It studied networked linear control systems and networked nonlinear control systems. Firstly, time-driven predictive controller is used to compensate for the networked-induced problems of a class of networked linear control systems while robustly stabilizing the closed-loop system. Secondly, event-driven predictive controller is applied to networked linear control system and NNCS and the work goes on to solve the robust control problem. The event-driven predictive controller brings great benefits to NCS implementation: it makes the synchronization of the clocks of the process and the controller unnecessary and it avoids measuring the exact values of the individual components of the network induced time-delay. This work developed the theory of stability analysis and robust synthesis of NCS and NNCS. The robust stability analysis and robust synthesis of a range of different system configurations have been thoroughly studied. A series of methods have been developed to handle the stability analysis and controller design for NCS and NNCS. The stability of the closed-loop of NCS has been studied by transforming it into that of a corresponding augmented system. It has been proved that if some equality conditions are satisfied then the closed-loop of NCS is stable for an upper-bounded random time delay and data dropouts. The equality conditions can be incorporated into a sub-optimal problem. Solving the sub-optimal problem gives the controller parameters and thus enables the synthesis of NCS. To simplify the calculation of solving the controller parameters, this thesis developed the relationship between networked nonlinear control system and a class of uncertain linear feedback control system. It proves that the controller parameters of some types of networked control system can be equivalently derived from the robust control of a class of uncertain linear feedback control system. The methods developed in this thesis for control design and robustness analysis have been validated by simulations or experiments.
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Wang, Limin. "Modeling and real-time feedback control of MEMS device." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3711.

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Thesis (Ph. D.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains v, 132 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 128-132).
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Brunke, Shelby Scott. "Nonlinear filtering and system identification algorithms for autonomous systems /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/7095.

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Hubbard, Paul J. "Hierarchical supervisory control systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0029/NQ64577.pdf.

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Ozcaldiran, Kadri. "Control of descriptor systems." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/13531.

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Books on the topic "Control systems"

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Anderson, Patrick. Control systems: Classical controls. Delhi: Global Media, 2009.

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Sundararajan, Dr D. Control Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98445-8.

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Kuntsevich, Vsevolod, Vyacheslav Gubarev, and Yuriy Kondratenko. Control Systems. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337706.

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Faghih, Nezameddin. Control Systems. Shiraz: Navid, 1999.

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K, Sinha N. Control systems. New York: CBS Publishing, 1986.

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K, Sinha N. Control systems. New York: Holt, Rinehart and Winston, 1986.

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Jagan, N. C. Control systems. 2nd ed. Hyderabad: BS Publications, 2008.

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Caldwell, Raymond. Control systems. Tonbridge: Hands On, 1997.

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K, Sinha N. Control systems. 2nd ed. New York: Wiley & Sons, 1994.

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G, Singh Madan, ed. Systems & control encyclopedia. Oxford, England: Pergamon Press, 1990.

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Book chapters on the topic "Control systems"

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Wilkie, Jacqueline, Michael Johnson, and Reza Katebi. "Simple systems: second-order systems." In Control Engineering, 173–95. London: Macmillan Education UK, 2002. http://dx.doi.org/10.1007/978-1-4039-1457-6_7.

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Dolan, Thomas J. "Control Systems." In Magnetic Fusion Technology, 313–75. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5556-0_7.

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Engelberger, Joseph F. "Control Systems." In Robotics in Service, 30–44. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1099-7_3.

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Wens, Mike, and Michiel Steyaert. "Control Systems." In Design and Implementation of Fully-Integrated Inductive DC-DC Converters in Standard CMOS, 169–212. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1436-6_5.

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Rabbath, C. A., and N. Léchevin. "Control Systems." In Discrete-Time Control System Design with Applications, 1–11. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9290-0_1.

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López, César Pérez. "Control Systems." In MATLAB Control Systems Engineering, 77–143. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0289-0_3.

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Genta, Giancarlo, and Lorenzo Morello. "Control Systems." In Mechanical Engineering Series, 323–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35635-4_6.

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Damić, Vjekoslav, and John Montgomery. "Control Systems." In Mechatronics by Bond Graphs, 333–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-49004-4_8.

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Silvis-Cividjian, Natalia. "Control Systems." In Undergraduate Topics in Computer Science, 31–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51655-4_3.

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Conrad, Albert R. "Control Systems." In SpringerBriefs in Astronomy, 39–87. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7058-8_5.

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Conference papers on the topic "Control systems"

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Khalil, Ashraf, and Jihong Wang. "Robust stabilization of Networked Control Systems using the Markovian jump system approach." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334649.

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Zhu, Q. M., S. Ghauri, and Jing Na. "U-model based control system formulisation and design for wind energy conversion systems." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737618.

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Gazi, Peymon, Mo Jamshidi, Aleksandar Jevtić, and Diego Andina. "A mechatronic system design case study: Control of a robotic swarm using networked control algorithms." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482439.

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Moseley, Callum, Tom Shenton, Ben Neaves, Paolo Paoletti, and Philip Fulcher. "Nonlinearity Detection in Dynamical Systems." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516892.

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Kersting, Stefan, and Marin Buss. "Online identification of piecewise affine systems." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915120.

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Gershon, E. "Delayed Systems - H∞ Preview Tracking Control." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516863.

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Najafi, M., F. Sheikholeslam, and S. Hosseinnia. "Robust predictor for uncertain dead time systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334783.

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Yang, Xin-Rong, and Guo-Ping Liu. "Output consensus of linear multi-agent systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334785.

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Drummond, Ross, and Stephen R. Duncan. "Externally Positive, Passive and Negative Imaginary Systems." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516744.

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Gahinet, Pascal, and Pierre Apkarian. "Frequency-domain tuning of fixed-structure control systems." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334626.

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Reports on the topic "Control systems"

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Byrnes, Christopher I., and Alberto Isidori. Nonlinear Control Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada567983.

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Byrnes, Christopher I., and Alberto Isidori. Nonlinear Control Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada424276.

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Plocki, Pete. Fire Control Systems. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada386365.

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Byrnes, Christopher I., and Alberto Isidori. Nonlinear Control Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada471765.

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Lemmon, Michael. Supervisory Control of Networked Control Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada442404.

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Quintero, Richard. A real-time control system methodology for developing intelligent control systems. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4936.

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Robert P. Evans. Control Systems Security Center Comparison Study of Industrial Control System Standards against the Control Systems Protection Framework Cyber-Security Requirements. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/911771.

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Wu, N. E. Control Reconfiguration of Command and Control Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada462717.

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Stouffer, Keith, Joe Falco, and Karen Scarfone. Guide to Industrial Control Systems (ICS) Security : Supervisory Control and Data Acquisition (SCADA) Systems, Distributed Control Systems (DCS), and Other Control System Configurations such as Programmable Logic Controllers (PLC). National Institute of Standards and Technology, May 2013. http://dx.doi.org/10.6028/nist.sp.800-82r1.

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Stouffer, Keith, Joe Falco, and Karen Scarfone. Guide to Industrial Control Systems (ICS) Security - Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and other control system configurations such as Programmable Logic Controllers (PLC). Gaithersburg, MD: National Institute of Standards and Technology, June 2011. http://dx.doi.org/10.6028/nist.sp.800.82.

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