Optimal estimation with limited measurements

Imer, O.C.; Başar, Tamer

doi:10.1504/ijscc.2010.031156

Cited by 92 publications

(75 citation statements)

References 11 publications

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“…Recall that the system is stable if and only if the system cost (3) is bounded. Hence, by (12), to guarantee the system stability, we only need to prove that both EOEP c and S 1 are bounded. We first prove by induction that EOEP c is bounded when (15) holds.…”

Section: Resultsmentioning

confidence: 99%

“…There has been a significant amount of research devoted to the analysis of WNCS under unreliable wireless communications [9][10][11][12]. Focusing on the state estimation problem under random measurement packet losses, Sinopoli et al first prove the existence of a critical packet reception rate above which the boundedness of the estimation error covariance can be ensured in the mean square sense [13].…”

Section: Introductionmentioning

confidence: 99%

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Optimal controller location in wireless networked control systems

Xin

Cao

Chen

et al. 2013

Intl J Robust & Nonlinear

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SUMMARYThe control performance of wireless networked control systems (WNCS) has been shown to heavily depend on the packet delivery quality of both the sensor‐to‐controller and controller‐to‐actuator communications. Such quality relies on the relative distance between the wireless transmitter and receiver, which naturally raises the challenging problem of controller placement in WNCS for optimal control performance. In this paper, we investigate the optimal controller location (OCL) problem in WNCS based on linear‐quadratic‐Gaussian control strategy. For the one‐hop network case where the controller can only be placed at either the sensor side or the actuator side, we derive a simple yet effective criterion to determine the OCL. For the more general multi‐hop case where the controller can be located at either one of the sensors, relays, or actuators, we obtain the necessary and sufficient condition under which the closed‐loop system is guaranteed to be stable. On the basis of these results, we further transform the OCL problem into an optimization problem that can be solved efficiently. Numerical results are provided to verify our analysis. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal controller location in wireless networked control systems

Xin

Cao

Chen

et al. 2013

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…- [10], where it is assumed that the encoder transmits real-valued samples of the input process and that the communication is subject to a sampling frequency constraint or a transmission cost. The causal sampling and estimation policies that achieve the optimal tradeoff between the sampling frequency and the distortion have been studied for the following discrete-time processes: the i.i.d process [1]; the Gauss-Markov process [2]; the partially observed Gauss-Markov process [3]; and, the first-order autoregressive Markov process X t+1 = aX t + V t driven by an i.i.d. process {V t } with unimodal and even distribution [4] [5].…”

Section: Related Work Includes [1]mentioning

confidence: 99%

“…Now, we proceed to show that (a)-(c) hold for all t ∈ [s, ess sup(τ i+1 )), given any s ∈ Supp(τ i ) using real induction. To verify that the condition (1) in Lemma 4 holds, we need to show that (a)-(c) hold for t = s. This is trivial since…”

Section: Lemma 3 ([2 Lemma 4])mentioning

confidence: 99%

Optimal Causal Rate-Constrained Sampling of the Wiener Process

Guo

Kostina

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Consider the following communication scenario. An encoder observes a stochastic process and causally decides when and what to transmit about it, under a constraint on bits transmitted per second. A decoder uses the received codewords to causally estimate the process in real time. We aim to find the optimal encoding and decoding policies that minimize the end-to-end estimation mean-square error under the rate constraint. For * N. Guo and V. Kostina are with the

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“…In [12] an stochastic approach is taken to estimate the states of a Lipschitz nonlinear system with time-varying delays in the states.[13] finds a minimum data transmission rate for the convergent estimation of a process with a specific distribution. [14] introduces a two-agent scheme, namely observer and estimator, under communication constraints. The former is located by the sensor and is responsible for evaluating sensor data and transmitting the information.…”

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confidence: 99%

filtering of nonlinear plants over networks

Charandabi

Marquez

2013

Intl J Robust & Nonlinear

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SummaryIn this paper, we consider the filtering problem for Lipschitz systems in a networked environment. We assume that the measurements transmitted over the network are subject to quantization, uncertain delays and communication constraints. We first analytically demonstrate how each of the these issues affect the filtering problem. Second, we tackle the filter design as an optimization problem with LMI constraints. The optimization maximizes the Lipschitz constant and thus the region of attraction for which the filter is stable and an scriptHMathClass-rel∞ bound is satisfied by the error system. Copyright © 2013 John Wiley & Sons, Ltd.

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Optimal estimation with limited measurements

Cited by 92 publications

References 11 publications

Optimal controller location in wireless networked control systems

Optimal controller location in wireless networked control systems

Optimal Causal Rate-Constrained Sampling of the Wiener Process

filtering of nonlinear plants over networks

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