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273
Consensus and cooperation in networked multiagent systems
 PROCEEDINGS OF THE IEEE
"... This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview of ..."
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Cited by 772 (2 self)
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This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in smallworld networks, Markov processes and gossipbased algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with latticetype nearest neighbor interactions. Simulation results are presented that demonstrate the role of smallworld effects on the speed of consensus algorithms and cooperative control of multivehicle formations.
A survey of recent results in networked control systems
 PROCEEDINGS OF THE IEEE
, 2007
"... Networked Control Systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. In this paper we review several recent results on estimation, analysis, and controller synthesis for NCSs. The re ..."
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Cited by 281 (11 self)
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Networked Control Systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. In this paper we review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packetrates, sampling, network delay and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies.
Foundations of control and estimation over lossy networks
 PROCEEDINGS OF THE IEEE
, 2007
"... When data are transmitted to an estimationcontrol unit over a network, and control commands are issued to subsystems over the same network, both observation and control packets may be lost or delayed. This process can be modeled by assigning probabilities to successfully receive packets. Determini ..."
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Cited by 140 (26 self)
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When data are transmitted to an estimationcontrol unit over a network, and control commands are issued to subsystems over the same network, both observation and control packets may be lost or delayed. This process can be modeled by assigning probabilities to successfully receive packets. Determining the impact of this uncertainty on the feedbackloop requires a generalization of classical control theory. This paper presents the foundations of such new theory. Motivations and overview of the efforts of different research groups are described first. Then, novel contributions of the authors are presented. These include showing threshold behaviors which are governed by the uncertainty parameters of the communication network: for network protocols where successful transmissions of packets is acknowledged at the receiver (e.g. TCPlike protocols), there exists critical probabilities for the successful delivery of packets, below which the optimal controller fails to stabilize the system. Furthermore, for these protocols, the separation principle holds and the optimal LQG control is a linear function of the estimated state. In stark contrast, it is shown that when there is no acknowledgement of successful delivery of control packets (e.g. UDPlike protocols), the LQG optimal controller is in general nonlinear.
On a Stochastic Sensor Selection Algorithm with Applications in Sensor Scheduling and Sensor Coverage
 AUTOMATICA
, 2006
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Kalman filtering with partial observation losses
 IEEE Trans. on Autom. Control
, 2004
"... We study the Kalman filtering problem when part or all of the observation measurements are lost in a random fashion. Pioneering work has recently addressed the Kalman filtering problem with intermittent observations, where the observation measurements are either received in full or completely lost. ..."
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Cited by 72 (10 self)
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We study the Kalman filtering problem when part or all of the observation measurements are lost in a random fashion. Pioneering work has recently addressed the Kalman filtering problem with intermittent observations, where the observation measurements are either received in full or completely lost. Partial observation losses can occur in a distributed control system where measurements are taken at different sensors that are at different physical locations or one sensor needs to send its data in multiple packets. We formulate the Kalman filtering problem with partial observation losses and derive the Kalman filter updates with partial observation measurements. We show that with these partial measurements the Kalman filter and its error covariance matrix iteration become stochastic, since they now depend on the random packet arrivals of the sensor measurements, which can be lost or delayed when transmitted over a communication network. The communication network needs to provide a sufficient throughput for each of the sensor measurements in order to guarantee the stability of the Kalman filter, where the throughput captures the rate of the sensor measurements correctly received. We investigate the statistical convergence properties of the error covariance matrix iteration as a function of the throughput of the sensor measurements. A throughput region that guarantees the convergence of the error covariance matrix is found by solving a feasibility problem of a Linear Matrix Inequality (LMI). We also find an unstable throughput region such that the state estimation error of the Kalman filter is unbounded. When the Kalman filter is stable, the expected error covariance matrix is bounded both from above and from below. The results are illustrated with some simple numerical examples. I.
Optimal LQG control across a packetdropping link
 IEEE Transactions on Automatic Control
, 2004
"... We examine two special cases of the problem of optimal Linear Quadratic Gaussian control of a system whose state is being measured by sensors that communicate with the controller over packetdropping links. We pose the problem as an information transmission problem. Using a separation principle, we ..."
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Cited by 71 (6 self)
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We examine two special cases of the problem of optimal Linear Quadratic Gaussian control of a system whose state is being measured by sensors that communicate with the controller over packetdropping links. We pose the problem as an information transmission problem. Using a separation principle, we decompose the problem into a standard LQR statefeedback controller design, along with an optimal encoderdecoder design for propagating and using the information across the unreliable link. Our design is optimal among all causal algorithms for any arbitrary packet drop pattern. Further, the solution is appealing from a practical point of view because it can be implemented as a small modification of an existing LQG control design.
Information fusion for wireless sensor networks
 Methods, models, and classifications, ACM Computing Surveys, Volume 39, Issue 3, Article 9
, 2007
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Distributing the Kalman filters for largescale systems
 IEEE Trans. on Signal Processing, http://arxiv.org/pdf/0708.0242
"... Abstract—This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, largescale,dimensional, dynamical system monitored by a network of sensors. Local Kalman filters are implemented ondimensional subsystems,, obtained by spatially decomposing the largescale sys ..."
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Cited by 54 (13 self)
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Abstract—This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, largescale,dimensional, dynamical system monitored by a network of sensors. Local Kalman filters are implemented ondimensional subsystems,, obtained by spatially decomposing the largescale system. The distributed Kalman filter is optimal under an th order Gauss–Markov approximation to the centralized filter. We quantify the information loss due to this thorder approximation by the divergence, which decreases as increases. The order of the approximation leads to a bound on the dimension of the subsystems, hence, providing a criterion for subsystem selection. The (approximated) centralized Riccati and Lyapunov equations are computed iteratively with only local communication and loworder computation by a distributed iterate collapse inversion (DICI) algorithm. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter. Nowhere in the network, storage, communication, or computation ofdimensional vectors and matrices is required; only dimensional vectors and matrices are communicated or used in the local computations at the sensors. In other words, knowledge of the state is itself distributed. Index Terms—Distributed algorithms, distributed estimation, information filters, iterative methods, Kalman filtering, largescale systems, matrix inversion, sparse matrices. I.
Optimal control of LTI systems over unreliable communication links
, 2006
"... In this paper, optimal control of linear timeinvariant (LTI) systems over unreliable communication links is studied. The motivation of the problem comes from growing applications that demand remote control of objects over Internettype or wireless networks where links are prone to failure. Dependin ..."
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Cited by 47 (3 self)
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In this paper, optimal control of linear timeinvariant (LTI) systems over unreliable communication links is studied. The motivation of the problem comes from growing applications that demand remote control of objects over Internettype or wireless networks where links are prone to failure. Depending on the availability of acknowledgment (ACK) signals, two different types of networking protocols are considered. Under a TCP structure, existence of ACK signals is assumed, unlike the UDP structure where no ACK packets are present. The objective here is to meansquare (m.s.) stabilize the system while minimizing a quadratic performance criterion when the information flow between the controller and the plant is disrupted due to link failures, or packet losses. Sufficient conditions for the existence of stabilizing optimal controllers are derived.
Data Transmission over Networks for Estimation and Control
"... We consider the problem of controlling a linear time invariant process when the controller is located at a location remote from where the sensor measurements are being generated. The communication from the sensor to the controller is supported by a communication network with arbitrary topology compo ..."
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Cited by 40 (8 self)
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We consider the problem of controlling a linear time invariant process when the controller is located at a location remote from where the sensor measurements are being generated. The communication from the sensor to the controller is supported by a communication network with arbitrary topology composed of analog erasure channels. Using a separation principle, we prove that the optimal LQG controller consists of an LQ optimal regulator along with an estimator that estimates the state of the process across the communication network mentioned above. We then determine the optimal information processing strategy that should be followed by each node in the network so that the estimator is able to compute the best possible estimate in the minimum mean squared error sense. The algorithm is optimal for any packetdropping process and at every time step, even though it is recursive and hence requires a constant amount of memory, processing and transmission at every node in the network per time step. For the case when the packet drop processes are memoryless and independent across links, we analyze the stability properties and the performance of the closed loop system. The algorithm is an attempt to escape the more commonly used viewpoint of treating a network of communication links as a single endtoend link with the probability of successful transmission determined by some measure of the reliability of the network. I.