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

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 channe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on automatic control Jg. 62; H. 6; S. 3054 - 3061
Hauptverfasser: Dey, Subhrakanti, Chiuso, Alessandro, Schenato, Luca
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Channel estimation
Channels
Co-design
Coding
Control systems
Control theory
Control under communication constraints
Decoding
Design engineering
Encoding
Feedback
Feedback control
Finite capacity
Innovations
linear quadratic Gaussian (LQG) control
Marketing
Mathematical analysis
Measurement
Modulation
Noise prediction
Optimization
Packet loss
packet losses
Probability theory
quantization
Quantization (signal)
Receivers
State space models
Transmitters
ISSN:0018-9286, 1558-2523, 1558-2523
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9286
1558-2523
1558-2523
DOI:10.1109/TAC.2017.2674024