Data Rate Theorem for Stabilization Over Time-Varying Feedback Channels

Minero, Paolo; Franceschetti, Massimo; Dey, Subhrakanti; Nair, Girish N.

doi:10.1109/tac.2008.2010887

Cited by 183 publications

(183 citation statements)

References 24 publications

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“…We note that for erasure channels and noiseless channels, one can obtain tight converse theorems using Theorem 6.18 (see [36] and [64]). For general DMCs, however, a tight converse result on quadratic stabilizability is not yet available.…”

Section: Remark 69mentioning

confidence: 97%

“…Particularly related references include [11, 28-30, 32, 37, 42, 43, 45, 46, 54, 55, 59]. In the context of discrete channels, many of these papers considered a bounded noise assumption, except notably [30,36,37,55,64], and [56]. We refer the reader to [32] and [60] for a detailed literature review.…”

Section: Problem P2: Characterization Of Information Channels For Stamentioning

confidence: 99%

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Design of Information Channels for Optimization and Stabilization in Networked Control

Yüksel

2014

Information and Control in Networks

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In stochastic control, typically a partial observation model/channel is given and one looks for a control policy for optimization or stabilization. Consider a single-agent dynamical system described by the discrete-time equations1)for (Borel measurable) functions f, g, with {w t } being an independent and identically distributed (i.i.d.) system noise process and {v t } an i.i.d. measurement disturbance process, which are independent of x 0 and each other. Here, x t ∈ X, y t ∈ Y, u t ∈ U, where we assume that these spaces are Borel subsets of finite dimensional Euclidean spaces. In (6.2), we can view g as inducing a measurement channel Q, which is a stochastic kernel or a regular conditional probability measure from X to Y in the sense that Q(·|x) is a probability measure on the (Borel) σ -algebra B(Y) on Y for every x ∈ X, and Q(A|·) : ] is a Borel measurable function for every A ∈ B(Y).In networked control systems, the observation channel described above itself is also subject to design. In a more general setting, we can shape the channel input by coding and decoding. This chapter is concerned with design and optimization of such channels.We will consider a controlled Markov model given by (6.1). The observation channel model is described as follows: This system is connected over a noisy channel with a finite capacity to a controller, as shown in Fig. 6.1. The controller has

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Section: Remark 69mentioning

confidence: 97%

Section: Problem P2: Characterization Of Information Channels For Stamentioning

confidence: 99%

Design of Information Channels for Optimization and Stabilization in Networked Control

Yüksel

2014

Information and Control in Networks

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“…1. Similar block diagram that are concerned with communication imperfections only from system to remote controller has been considered in many research papers, such as [6], [8]- [13]. This block diagram can correspond to the smart oil drilling system that uses down hole telemetry [2] and also tele-operation systems of micro autonomous vehicles [1].…”

Section: B Paper Contributionsmentioning

confidence: 99%

Reference Tracking of Nonlinear Dynamic Systems over AWGN Channel Using Describing Function

Parsa

Farhadi

2018

Scientia Iranica

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This paper presents a new technique for mean square asymptotic reference tracking of nonlinear dynamic systems over Additive White Gaussian Noise (AWGN) channel. The nonlinear dynamic system has periodic outputs to sinusoidal inputs and is cascaded with a bandpass filter acting as encoder.Using the describing function method, the nonlinear dynamic system is represented by an equivalent linear dynamic system. Then, for this system, a mean square asymptotic reference tracking technique including an encoder, decoder and a controller is presented. It is shown that the proposed reference tracking technique results in mean square asymptotic reference tracking of nonlinear dynamic systems over AWGN channel. The satisfactory performance of the proposed reference tracking technique is illustrated using practical example simulations.

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“…References [21], [20] and [8] studied control over noisy channels and obtained fundamental lower bounds for information transmission. Recently, [13] studied the problem concerning time-varying channels and provided a necessity and sufficiency result for boundedness of second moments.…”

Section: A Literature Review With Regard To Stabilizationmentioning

confidence: 99%

A random time stochastic drift result and application to stochastic stabilization over noisy channels

Yüksel

2009

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-A random time state-dependent drift result leading to various forms of stochastic stability for a Markov Chain is presented. Application to a network stabilization problem is studied. In particular, we observe that, for control over a discrete erasure channel with feedback, for recurrence or stochastic stability, it suffices to have the capacity being greater than the logarithm of the unstable eigenvalue. For the finiteness of a second moment, however, more stringent criteria are needed.

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Data Rate Theorem for Stabilization Over Time-Varying Feedback Channels

Cited by 183 publications

References 24 publications

Design of Information Channels for Optimization and Stabilization in Networked Control

Design of Information Channels for Optimization and Stabilization in Networked Control

Reference Tracking of Nonlinear Dynamic Systems over AWGN Channel Using Describing Function

A random time stochastic drift result and application to stochastic stabilization over noisy channels

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