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101
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.
Sensor Selection via Convex Optimization
 IEEE Transactions on Signal Processing
, 2009
"... We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not ..."
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Cited by 89 (2 self)
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We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not practical unless m and k are small. In this paper we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m3 operations; for m = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2 GHz personal computer. 1
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.
SOIKF: Distributed Kalman Filtering With LowCost Communications Using the Sign of Innovations
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2006
"... When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations—a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic proc ..."
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Cited by 57 (13 self)
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When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations—a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic processes, whereby the low communication cost is effected by requiring the transmission of a single bit per observation. Following a Kalman filtering (KF) approach, we develop recursive algorithms for distributed state estimation based on the sign of innovations (SOI). Even though SOIKF can afford minimal communication overhead, we prove that in terms of performance and complexity it comes very close to the clairvoyant KF which is based on the analogamplitude observations. Reinforcing our conclusions, we show that the SOIKF applied to distributed target tracking based on distanceonly observations yields accurate estimates at low communication cost.
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.
Distributed control of robotic networks: a mathematical approach to motion coordination algorithms
, 2009
"... (i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 ..."
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Cited by 38 (1 self)
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(i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 “Distributed Control of Robotic Networks ” by F. Bullo, J. Cortés and S. Martínez
MultiUAV dynamic routing with partial observations using restless bandit allocation indices
 in American Control Conference
, 2008
"... Motivated by the type of missions currently performed by unmanned aerial vehicles, we investigate a discrete dynamic vehicle routing problem with a potentially large number of targets and vehicles. Each target is modeled as an independent twostate Markov chain, whose state is not observed if the ta ..."
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Cited by 32 (3 self)
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Motivated by the type of missions currently performed by unmanned aerial vehicles, we investigate a discrete dynamic vehicle routing problem with a potentially large number of targets and vehicles. Each target is modeled as an independent twostate Markov chain, whose state is not observed if the target is not visited by some vehicle. The goal for the vehicles is to collect rewards obtained when they visit the targets in a particular state. This problem can be seen as a type of restless bandits problem, although we operate here under partial information. We compute an upper bound on the achievable performance and obtain in closed form an index policy proposed by Whittle. Simulation results provide evidence for the outstanding performance
Towards Receding Horizon Networked Control”, Systems and Control
 Towards Receding Horizon Networked Control”, Allerton conference on communication, control and computing
, 2006
"... Abstract — This paper deals with the design of control systems over lossy networks. A network is assumed to exist between the sensor and the controller and between the latter and the actuator. Packets are dropped according to a Bernoulli independent process, with γ and µ being the probabilities of l ..."
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Cited by 17 (3 self)
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Abstract — This paper deals with the design of control systems over lossy networks. A network is assumed to exist between the sensor and the controller and between the latter and the actuator. Packets are dropped according to a Bernoulli independent process, with γ and µ being the probabilities of losing an observation packet and a control packet respectively, at time any instant t. A receding horizon control scheme is proposed for the Linear Quadratic Control (LQG) problem. At each instant N future control inputs are sent in addition to the current one. Under this scheme the separation of estimation and control is shown and stability conditions, dependent on loss probabilities, are provided. Simulations show how the overall performance, in terms of lower cost, increases with the length of the horizon. I.
Kalman filtering with intermittent observations: Weak convergence to a stationary distribution. Accepted for publication
 in the IEEE Transactions on Automatic Control with mandatory revisions
, 2009
"... The paper studies the asymptotic behavior of Random Algebraic Riccati Equations (RARE) arising in Kalman filtering when the arrival of the observations is described by a Bernoulli i.i.d. process. We model the RARE as an orderpreserving, strongly sublinear random dynamical system (RDS). Under a suff ..."
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Cited by 16 (4 self)
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The paper studies the asymptotic behavior of Random Algebraic Riccati Equations (RARE) arising in Kalman filtering when the arrival of the observations is described by a Bernoulli i.i.d. process. We model the RARE as an orderpreserving, strongly sublinear random dynamical system (RDS). Under a sufficient condition, stochastic boundedness, and using a limitset dichotomy result for orderpreserving, strongly sublinear RDS, we establish the asymptotic properties of the RARE: the sequence of random prediction error covariance matrices converges weakly to a unique invariant distribution, whose support exhibits fractal behavior. In particular, this weak convergence holds under broad conditions and even when the observations arrival rate is below the critical probability for mean stability. We apply the weakFeller property of the Markov process governing the RARE to characterize the support of the limiting invariant distribution as the topological closure of a countable set of points, which, in general, is not dense in the set of positive semidefinite matrices. We use the explicit characterization of the support of the invariant distribution and the almost sure ergodicity of the sample paths to easily compute the moments of the invariant distribution. A one dimensional example illustrates that the support is a fractured subset of the nonnegative reals with selfsimilarity properties.
Decentralized Quantized Kalman Filtering With Scalable Communication Cost
"... Abstract—Estimation and tracking of generally nonstationary Markov processes is of paramount importance for applications such as localization and navigation. In this context, ad hoc wireless sensor networks (WSNs) offer decentralized Kalman filtering (KF) based algorithms with documented merits over ..."
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Cited by 15 (7 self)
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Abstract—Estimation and tracking of generally nonstationary Markov processes is of paramount importance for applications such as localization and navigation. In this context, ad hoc wireless sensor networks (WSNs) offer decentralized Kalman filtering (KF) based algorithms with documented merits over centralized alternatives. Adhering to the limited power and bandwidth resources WSNs must operate with, this paper introduces two novel decentralized KF estimators based on quantized measurement innovations. In the first quantization approach, the region of an observation is partitioned into contiguous, nonoverlapping intervals where each partition is binary encoded using a block of bits. Analysis and Monte Carlo simulations reveal that with minimal communication overhead, the meansquare error (MSE) of a novel decentralized KF tracker based on 23 bits comes stunningly close to that of the clairvoyant KF. In the second quantization approach, if intersensor communications can afford bits at time, then the th bit is iteratively formed using the sign of the difference between the th observation and its estimate based on past observations (up to time I) along with previous bits (up to I) of the current observation. Analysis and simulations show that KFlike tracking based on bits of iteratively quantized innovations communicated among sensors exhibits MSE performance identical to a KF based on analogamplitude observations applied to an observation model with noise variance increased by a factor of ‘I