LQG-like control of scalar systems over communication channels: The role of data losses, delays and SNR limitations

Chiuso, Alessandro; Laurenti, Nicola; Schenato, Luca; Zanella, Andréa

doi:10.1016/j.automatica.2014.10.011

Cited by 11 publications

(13 citation statements)

References 41 publications

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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%

“…However, the optimal strategy in the presence of imperfect channel feedback remains elusive and only sensible heuristic have been proposed in [19]. In the context of closed loop unstable control system, simultaneous analysis of packet loss and quantization has been studied in [20] assuming that the transmitter simply forwards a quantized version of the raw measurement.…”

Section: Introductionmentioning

confidence: 99%

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Linear encoder-decoder-controller design over channels with packet loss and quantization noise

Dey¹,

Chiuso²,

Schenato³

2015

2015 European Control Conference (ECC)

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In this paper we consider the problem of designing coding and decoding schemes for linear control design of a scalar unstable stochastic linear system in the presence of a wireless communication channel between the sensor and the estimator. In particular, we consider a communication channel which is prone to packet loss and includes quantization noise due to its limited capacity. We first study the case of perfect channel feedback, where the transmitter is aware of the quantization noise and the packet loss history of the channel. We show that in this case, the optimal strategy among all possible linear encoders corresponds to the transmission of the Kalman filter innovation (the difference between the filtered state estimate at the transmitter and the predicted state estimate at the receiver) similarly to the differential pulse-code modulation (DPCM). Although the critical Signalto-Quantization Noise Ratio (SQNR) required for stabilizing the system is the same for innovation forwarding as well as measurement forwarding at the transmitter, the latter is strictly suboptimal in terms of control performance. For the case of imperfect feedback, we assume that the channel feedback or acknowledgement is randomly lost with a certain erasure probability, rendering the transmitter ignorant of the control action taken by the receiver and subsequently applied to the plant. We propose several heuristic strategies for a suboptimal Kalman filter design at the transmitter based on estimation of the channel feedback status and compare their performances via numerical simulation studies.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Linear encoder-decoder-controller design over channels with packet loss and quantization noise

Dey¹,

Chiuso²,

Schenato³

2015

2015 European Control Conference (ECC)

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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%

Feedback Control Over Lossy SNR-Limited Channels: Linear Encoder–Decoder–Controller Design

Dey

Chiuso

Schenato

2017

IEEE Trans. Automat. Contr.

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In this paper, we consider the problem of encoding and decoding codesign for linear feedback control of a scalar, possibly unstable, stochastic linear system when the sensed signal is to be transmitted over a finite capacity communication channel. In particular, we consider a limited capacity channel which transmits quantized data and is subject to packet losses. We first characterize the optimal strategy when perfect channel feedback is available, i.e., the transmitter has perfect knowledge of the packet loss history. This optimal scheme, innovation forwarding hereafter, is reminiscent of differential pulse-code modulation schemes adapted to deal with state space models, and is strictly better than a scheme which simply transmits the measured data, called measurement forwarding (MF) hereafter. Comparison in terms of control cost as well as of critical regimes, i.e., regimes where the cost is not finite, are provided. We also consider and compare two popular suboptimal schemes from the existing literature, based on 1) state estimate forwarding and 2) measurement forwarding, which ignore quantization effects in the associated estimator and controller design. In particular, it is shown that surprisingly the suboptimal MF strategy is always better then the suboptimal state forwarding strategy for small signal-to-quantization-noise-ratios.

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Introduction

2023

Control Over Communication Networks

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LQG-like control of scalar systems over communication channels: The role of data losses, delays and SNR limitations

Cited by 11 publications

References 41 publications

Linear encoder-decoder-controller design over channels with packet loss and quantization noise

Linear encoder-decoder-controller design over channels with packet loss and quantization noise

Feedback Control Over Lossy SNR-Limited Channels: Linear Encoder–Decoder–Controller Design

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