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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. [ABSTRACT FROM AUTHOR]

SUMMARY This work focuses on tracking and system identification of systems with regime-switching parameters, which are modeled by a Markov process. It introduces a framework for persistent identification problems that encompass many typical system uncertainties, including parameter switching, stochastic observation disturbances, deterministic unmodeled dynamics, sensor observation bias, and nonlinear model mismatch. In accordance with the 'frequency' of the parameter switching process, we divide the problems into two classes. For fast-switching systems, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains. Because accurately tracking real-time parameters in such systems is not possible because of the uncertainty principles, the effect of parameter switching is evaluated on their average by the stationary distribution of the Markovian chain and estimated by the least squares algorithms. We derive upper and lower bounds on identification errors, which characterize how identification accuracy depends on the earlier uncertainty terms. When the system parameters switch their values infrequently in a probabilistic sense, their values can be tracked based on input/output observations. Stochastic approximation algorithms with adaptive step sizes are used for such systems. Simulation studies are carried out to demonstrate that slowly varying parameters could be tracked with reasonable accuracy.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]