Optimal Control of Energy Resources for State Estimation Over Wireless Channels
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This brief introduces wireless communications ideas and techniques into the study of networked control systems. It focuses on state estimation problems in which sensor measurements (or related quantities) are transmitted over wireless links to a central observer. Wireless communications techniques are used for energy resource management in order to improve the performance of the estimator when transmission occurs over packet dropping links, taking energy use into account explicitly in Kalman filtering and control. The brief allows a reduction in the conservatism of control designs by taking advantage of the assumed. The brief shows how energy-harvesting-based rechargeable batteries or storage devices can offer significant advantages in the deployment of large-scale wireless sensor and actuator networks by avoiding the cost-prohibitive task of battery replacement and allowing self-sustaining sensor to be operation. In contrast with research on energy harvesting largely focused on resource allocation for wireless communication systems design, this brief optimizes estimation objectives such as minimizing the expected estimation error covariance. The resulting power control problems are often stochastic control problems which take into account both system and channel dynamics. The authors show how to pose and solve such design problems using dynamic programming techniques. Researchers and graduate students studying networked control systems will find this brief a helpful source of new ideas and research approaches. Alex S. Leong was born in Macau in 1980. He received the B.S. degree in mathematics and B.E. degree in electrical engineering in 2003, and the Ph.D. degree in electrical engineering in 2008, all from the University of Melbourne, Australia. He is currently a Research Associate at the University of Paderborn, Germany. He was with the Department of Electrical and Electronic Engineering at the University of Melbourne from 2008 to 2015. His research interests include networked control systems, signal processing for sensor networks, and statistical signal processing. Dr. Leong was the recipient of the L. R. East Medal from Engineers Australia in 2003, an Australian Postdoctoral Fellowship from the Australian Research Council in 2009, and a Discovery Early Career Researcher Award from the Australian Research Council in 2012. Daniel E. Quevedo holds the Chair in Automatic Control (Regelungs- und Automatisierungstechnik) at Paderborn University, Germany. He received Ingeniero Civil Electronico and M.Sc. degrees from the Universidad Tecnica Federico Santa Maria, Chile, in 2000. In 2005, he was awarded the Ph.D. degree from The University of Newcastle, Australia. Dr. Quevedo was supported by a full scholarship from the alumni association during his time at the Universidad Tecnica Federico Santa Maria and received several university-wide prizes upon graduating. He received the IEEE Conference on Decision and Control Best Student Paper Award in 2003 and was also a finalist in 2002. In 2009 he was awarded a five-year Research Fellowship from the Australian Research Council. Prof. Quevedo is Editor of the International Journal of Robust and Nonlinear Control and serves as Chair of the IEEE Control Systems Society Technical Committee on Networks & Communication Systems. His research interests are in automatic control, signal processing, and power electronics. Subhranki Dey was born in India in 1968. He received the B.Tech. and M.Tech. degrees from the Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, in 1991 and 1993, respectively, and the Ph.D. degree from the Department of Systems Engineering, Research School of Information Sciences and Engineering, Australian National University, Canberra, in 1996. He is currently a Professor with the Department of Engineering Sciences in Uppsala University, Sweden. Prior to this, he was a Professor with the Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Australia, from 2000 until early 2013. From September 1995 to September 1997, and September 1998 to February 2000, he was a Postdoctoral Research Fellow with the Department of Systems Engineering, Australian National University. From September 1997 to September 1998, he was a Postdoctoral Research Associate with the Institute for Systems Research, University of Maryland, College Park. His current research interests include networked.

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Optimal Control of Energy Resources for State Estimation Over Wireless Channels ab 53.49 EURO SpringerBriefs in Control, Automation and Robotics SpringerBriefs in Control, Automation and Robotics. 2018. Auflage, Optimal Control of Energy Resources for State Estimation Over Wireless Channels ab 53.49 EURO SpringerBriefs in Control, Automation and Robotics SpringerBriefs in Control, Automation and Robotics. 2018. Auflage.

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This brief introduces wireless communications ideas and techniques into the study of networked control systems. It focuses on state estimation problems in which sensor measurements (or related quantities) are transmitted over wireless links to a central observer. Wireless communications techniques are used for energy resource management in order to improve the performance of the estimator when transmission occurs over packet dropping links, taking energy use into account explicitly in Kalman filtering and control. The brief allows a reduction in the conservatism of control designs by taking advantage of the assumed. The brief shows how energy-harvesting-based rechargeable batteries or storage devices can offer significant advantages in the deployment of large-scale wireless sensor and actuator networks by avoiding the cost-prohibitive task of battery replacement and allowing self-sustaining sensor to be operation. In contrast with research on energy harvesting largely focused on resource allocation for wireless communication systems design, this brief optimizes estimation objectives such as minimizing the expected estimation error covariance. The resulting power control problems are often stochastic control problems which take into account both system and channel dynamics. The authors show how to pose and solve such design problems using dynamic programming techniques. Researchers and graduate students studying networked control systems will find this brief a helpful source of new ideas and research approaches.

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Optimal Control of Energy Resources for State Estimation Over Wireless Channels: This brief introduces wireless communications ideas and techniques into the study of networked control systems. It focuses on state estimation problems in which sensor measurements (or related quantities) are transmitted over wireless links to a central observer. Wireless communications techniques are used for energy resource management in order to improve the performance of the estimator when transmission occurs over packet dropping links, taking energy use into account explicitly in Kalman filtering and control. The brief allows a reduction in the conservatism of control designs by taking advantage of the assumed. The brief shows how energy-harvesting-based rechargeable batteries or storage devices can offer significant advantages in the deployment of large-scale wireless sensor and actuator networks by avoiding the cost-prohibitive task of battery replacement and allowing self-sustaining sensor to be operation. In contrast with research on energy harvesting largely focused on resource allocation for wireless communication systems design, this brief optimizes estimation objectives such as minimizing the expected estimation error covariance. The resulting power control problems are often stochastic control problems which take into account both system and channel dynamics. The authors show how to pose and solve such design problems using dynamic programming techniques. Researchers and graduate students studying networked control systems will find this brief a helpful source of new ideas and research approaches. Englisch, Taschenbuch.

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Optimal Control of Energy Resources for State Estimation Over Wireless Channels: The brief shows how energy-harvesting-based rechargeable batteries or storage devices can offer significant advantages in the deployment of large-scale wireless sensor and actuator networks by avoiding the cost-prohibitive task of battery replacement and allowing self-sustaining sensor to be operation. In contrast with research on energy harvesting largely focused on resource allocation for wireless communication systems design, this brief optimizes estimation objectives such as minimizing the expected estimation error covariance. The resulting power control problems are often stochastic control problems which take into account both system and channel dynamics. The authors show how to pose and solve such design problems using dynamic programming techniques. Englisch, Ebook.

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Optimal Control of Energy Resources for State Estimation Over Wireless Channels ab 43.49 EURO SpringerBriefs in Electrical and Computer Engineering, Optimal Control of Energy Resources for State Estimation Over Wireless Channels ab 43.49 EURO SpringerBriefs in Electrical and Computer Engineering.

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