Feedback systems with communications: integrated study of signal estimation, sampling, quantization, and feedback robustness

SUMMARY Feedback systems with communication channels encounter unique challenges. Communication channels mandate signal sampling and quantization, and introduce errors, data losses, and delays. Consequently, transmitted signals must be estimated. The signal estimation introduces a dynamic system that interacts with communication channels and affects the stability and performance of the feedback system. This paper studies interactions among communications, sampling, quantization, signal estimation, and feedback, in terms of fundamental stability and performance limitations. Typical empirical-measure-based algorithms are used for signal estimation under quantized observations. When the sampling interval and signal estimation step size are coordinated, the ODE approach for stochastic approximations provides a suitable platform for an integrated system analysis for signal estimation, sampling and quantization, and feedback robustness. Feedback design for enhancing robustness against communication uncertainty and signal estimation dynamics is studied under the new notion of stability margin under signal averaging. Fundamental limitations on noise attenuation in such an integrated system are derived. Copyright © 2013 John Wiley & Sons, Ltd.

[1]  Munther A. Dahleh,et al.  Feedback Control in the Presence of Noisy Channels: “Bode-Like” Fundamental Limitations of Performance , 2008, IEEE Transactions on Automatic Control.

[2]  P. Khargonekar,et al.  Non-Euclidian metrics and the robust stabilization of systems with parameter uncertainty , 1985 .

[3]  Tamer Basar,et al.  Optimal Signaling Policies for Decentralized Multicontroller Stabilizability Over Communication Channels , 2007, IEEE Transactions on Automatic Control.

[4]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[5]  Le Yi Wang,et al.  Quantized Identification With Dependent Noise and Fisher Information Ratio of Communication Channels , 2010, IEEE Transactions on Automatic Control.

[6]  Le Yi Wang,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[7]  Victor Solo,et al.  Stabilization and Disturbance Attenuation Over a Gaussian Communication Channel , 2010, IEEE Transactions on Automatic Control.

[8]  G. Yin,et al.  System Identification with Quantized Observations , 2010 .

[9]  Pierre Priouret,et al.  Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.

[10]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[11]  Andrea Garulli,et al.  Time complexity and input design in worst-case identification using binary sensors , 2007, 2007 46th IEEE Conference on Decision and Control.

[12]  H. Kushner,et al.  Stochastic Approximation and Recursive Algorithms and Applications , 2003 .

[13]  Benjamin C. Kuo,et al.  Digital Control Systems , 1977 .

[14]  Robin J. Evans,et al.  Feedback Control Under Data Rate Constraints: An Overview , 2007, Proceedings of the IEEE.

[15]  Le Yi Wang,et al.  Joint identification of plant rational models and noise distribution functions using binary-valued observations , 2006, Autom..

[16]  Harold J. Kushner,et al.  Approximation and Weak Convergence Methods for Random Processes , 1984 .

[17]  Bruce A. Francis,et al.  Feedback Control Theory , 1992 .

[18]  Richard H. Middleton,et al.  Fundamental limitations in control over a communication channel , 2008, Autom..

[19]  Andrey V. Savkin,et al.  The problem of LQG optimal control via a limited capacity communication channel , 2004, Syst. Control. Lett..

[20]  Kai Liu Stochastic Stability of Differential Equations in Abstract Spaces , 2022 .

[21]  Wei Xing Zheng,et al.  Signal estimation with binary-valued sensors , 2010, J. Syst. Sci. Complex..

[22]  R. Bass,et al.  Review: P. Billingsley, Convergence of probability measures , 1971 .

[23]  Le Yi Wang,et al.  System identification using binary sensors , 2003, IEEE Trans. Autom. Control..

[24]  Han-Fu Chen,et al.  Identification and Stochastic Adaptive Control , 1991 .

[25]  Michel Loève,et al.  Probability Theory I , 1977 .

[26]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .