2014
DOI: 10.1016/j.automatica.2014.10.011
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LQG-like control of scalar systems over communication channels: The role of data losses, delays and SNR limitations

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Cited by 11 publications
(13 citation statements)
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“…We show that the optimal strategy when full channel feedback is available at the transmitter is to send the difference from the estimated state at the transmitter and the predicted state at the receiver as in [19] and to build a Kalman filter and a state feedback with constant gain at the receiver as in [20]. However, although the performance is improved as compared to strategy proposed in [20], the stability region is the same. In the imperfect channel scenario, we propose a number of heuristics similarly to those proposed in [19].…”
Section: Introductionmentioning
confidence: 77%
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“…We show that the optimal strategy when full channel feedback is available at the transmitter is to send the difference from the estimated state at the transmitter and the predicted state at the receiver as in [19] and to build a Kalman filter and a state feedback with constant gain at the receiver as in [20]. However, although the performance is improved as compared to strategy proposed in [20], the stability region is the same. In the imperfect channel scenario, we propose a number of heuristics similarly to those proposed in [19].…”
Section: Introductionmentioning
confidence: 77%
“…In this work we extend the results of [19] and [20] by considering the possibility to pre-process the raw measurement at the transmitter. We show that the optimal strategy when full channel feedback is available at the transmitter is to send the difference from the estimated state at the transmitter and the predicted state at the receiver as in [19] and to build a Kalman filter and a state feedback with constant gain at the receiver as in [20].…”
Section: Introductionmentioning
confidence: 92%
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“…The interplay between control stability and nonidealities of communication channels has attracted considerable attention in the past decade, mainly driven by the success of wireless communication and its penetration into automation and control applications. From a theoretical perspective, we have witnessed the convergence of control theory, communication theory, and information theory which have led to remarkable and interesting results in terms of the ultimate performance limitations which take into account both the dynamical systems characteristic, typically their unstable eigenvalues and nonminimum phase zeros, and the channel characteristic, typically its capacity [1]- [11].…”
Section: Introductionmentioning
confidence: 99%
“…However, the optimal strategy in the presence of imperfect channel feedback remains elusive and only some sensible heuristic schemes have been proposed in [21]. In the context of closed loop unstable control system, simultaneous analysis of packet loss and quantization has been studied in [11] assuming that the transmitter simply forwards a quantized version of the raw measurement (measurement forwarding). Another related work is [22] which obtains minimum data rates for mean square stabilizability of linear systems over lossy channels.…”
Section: Introductionmentioning
confidence: 99%