Stabilizability of Stochastic Linear Systems with Finite Feedback Data Rates

By G. Nair, R. Evans. SIAM Journal of Control and Optimization, 2004

758 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/4be99cf81541520210a7ff00c9d5635704c925a7

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In Control and Systems Engineering

Citations in the corpus

Optimal LQG control across packet-dropping links (2007, background): “[27], Nair and Evans [25], and many others.”
Convergence Analysis of Quantized Primal-Dual Algorithms in Network Utility Maximization Problems (2016, background): “Proof: Follows directly from the first inequality in appendix C in [11]; alternatively, it can be derived from (8.”
Feedback stabilization over signal-to-noise ratio constrained channels (2004, methodology): “There is no apparent reason why the channel capacity required for output feedback stabilization should be equal to the data rate derived in [3, 7].”
Stability Analysis of Networked Control Systems Using a Switched Linear Systems Approach (2009, background): “For instance, the effects of quantization are studied in [5]–[9], of packet dropouts in [10]–[12], of time-varying transmission intervals and delays in [13]–[17], and [18]–[24], respectively, and of communication constraints in [25]–[28].”
Topological feedback entropy and Nonlinear stabilization (2004, background): “One possibility is to simply define it as the infimum data rate needed to be able to stabilize the plant, and indeed expressions for the infimum rate are available both for noiseless [3], [10]–[15] and stochastic linear systems.”
Cyber–Physical Systems: A Perspective at the Centennial (2012, background): “The minimum data rate needed for mean-square stabilizability of a system with both state and observation noises in treated in [68].”
Control and Communication Challenges in Networked Real-Time Systems (2007, background): “The constructive arguments used in the literature to prove that the data-rate inequality is a sufficient condition for practical stabilizability typically involve complex coding schemes; see, e.g., [93]–[95], [111], and [112].”
Networked Control Systems With Communication Constraints: Tradeoffs Between Transmission Intervals, Delays and Performance (2010, background): “Indeed, the effects of quantization are studied in [3], [12], [20], [22], [28], [36], [45], of packet dropouts in [41], [42], of time-varying transmission intervals and delays in [14], [32], and [10], [16], [24], [27], [35], [55], respectively , and of communication constraints in [2], [11], [26],…”
The Necessity and Sufficiency of Anytime Capacity for Stabilization of a Linear System Over a Noisy Communication Link—Part I: Scalar Systems (2006, background): “extended this to cover the case of unbounded disturbances and observation noise under suitable conditions [10], [11].”
Control: A perspective (2014, background): “For the control problem, it is of interest to determine the data rate needed to be provided by the channel in order to stabilize a given linear system [492]; this is also related to the problem of how to quantize measurements for the purpose of control [689,118].”