Control under communication constraints

Jain, Rajeev; Simsek, T.; Varaiya, Pravin

doi:10.1109/cdc.2002.1184366

Cited by 11 publications

(13 citation statements)

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“…Examples concern data exchange between a satellite and Earth surface station [16], or underwater autonomous sensors and the base station or vehicle [40], or a network of low power sensors and the central decision-maker.…”

Section: Observation 21mentioning

confidence: 99%

“…In the context of noisy DMC, the weaker r th moment observability/stabilizability was examined in [16,21,22,33,34,39] for scalar linear plants and uniformly bounded additive disturbances. Such an observability means that the expected value of the estimation error is kept bounded as time progresses.…”

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confidence: 99%

“…Recently there has been a great deal of research activity in this field; e.g., see [2,5,6,[13][14][15][16][18][19][20][21][22][23][24][25][26][27][28][29][30][31][33][34][35][36][39][40][41][42][43][46][47][48] and the literature therein. The issues of stabilization and state estimation via limited capacity channels are amongst the first arising in this area.…”

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confidence: 99%

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State estimation via limited capacity noisy communication channels

Matveev

2007

Math. Control Signals Syst.

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The paper addresses a state estimation problem involving communication errors and capacity constraints. Discrete-time partially observed linear systems perturbed by stochastic unbounded additive disturbances are studied. Unlike the classic theory, the sensor signals are communicated to the estimator over a limited capacity noisy digital link modeled as a stochastic discrete memoryless channel. It is shown that the capability of the noisy channel to ensure state estimation with a bounded in probability error is identical to its capability to transmit information with as small probability of error as desired. In other words, the classic Shannon capacity of the channel constitutes the boundary of the observability domain. It is shown that whenever the Shannon capacity bound is met, a reliable observation can be ensured by means of a state estimator consuming a bounded (as time progresses) computational complexity and memory per unit time. The corresponding state estimator is constructed explicitly and is based on the classic block coding approach, so that traditional block encoding-decoding procedures can be employed for its implementation.

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Section: Observation 21mentioning

confidence: 99%

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confidence: 99%

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State estimation via limited capacity noisy communication channels

Matveev

2007

Math. Control Signals Syst.

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“…We also specify the necessity of the above bound by showing that whenever it is trespassed h > c, any estimator diverges with the probability no less than 1−c/h. Finally we show that the basic estimation algorithm designed on the base of the perfect channel model [7], [21] diverges almost surely whenever the communication channel makes errors with arbitrarily small but positive probability. This underscores the need for a special design methodology taking into account the communication noise.…”

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confidence: 97%

“…In this paper, we show that for the general discrete memoryless channel, the Shannon's capacity is "responsible" for the convergence with arbitrarily high probability. An encoderdecoder pair for estimating the state of a one-dimensional linear system via a noisy binary symmetric channel was proposed in [7]. It was shown by simulation that the estimation error is bounded.…”

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An analogue of Shannon information theory for networked control systems: State estimation via a noisy discrete channel

Matveev

Savkin

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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The paper addresses a state estimation problem involving bit-rate communication capacity constraints. A discrete-time partially observed linear system is studied. Unlike the classic theory, the sensor signals are transmitted to the estimator over a noisy digital communication channel. A recursive coder-decoder state estimation scheme is proposed and investigated. It is shown that the classic Shannon's noisy channel capacity constitutes the border separating the cases where the reliable state estimation is and, respectively, is not possible with arbitrarily high probability. I. INTRODUCTIONThe standard assumption in the classical control theory is that data transmission required by the algorithm can be performed with infinite precision. However due to the growth in communication technology, it is becoming more common to employ digital finite capacity networks for exchange of information between plant components. Examples concern complex dynamical processes like advanced aircraft, spacecraft, automotive, industrial and defence systems, arrays of microactuators, and power control in mobile communication. Bandwidths communication constraints are often major obstacles to control system design by means of the classical theory. For instance as was shown in [20], design of control systems for platoons of underwater vehicles strongly highlights the need for control strategies that address explicitly the bandwidth limitation on communication between vehicles, which is severely restricted underwater. All these emerging applications motivate development of a new chapter of control theory that deals with networked systems and combines together the control and communication issues, taking into account all the limitations on communication between sensors, controllers, and actuators.Recently there was a good deal of research activity in this field; e.g., see [1], [3], [5-8], [13], [14], [16], [17], [21], [22]. Various schemes were proposed for stabilization and estimation via a limited capacity channel. The focus was on the channel quantization effects, and perfect (noiseless and undelayed) discrete channels were considered. This is in a sharp contrast with the classic communication theory, where limited capacity channels are modelled in terms of not only quantization effects but also channel errors and time delays. Moreover, many of the major results in this theory

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Sufficient Conditions for the Stabilizability of Multi-State Uncertain Systems, Under Information Constraints

Martins¹,

Dahleh²,

Elia

Informatics in Control, Automation and Robotics I

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Control under communication constraints

Cited by 11 publications

References 7 publications

State estimation via limited capacity noisy communication channels

State estimation via limited capacity noisy communication channels

An analogue of Shannon information theory for networked control systems: State estimation via a noisy discrete channel

Sufficient Conditions for the Stabilizability of Multi-State Uncertain Systems, Under Information Constraints

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