Stabilization of linear systems with limited information

By Nicola Elia, Sanjoy K. Mitter. 2001

1,821 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

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In Control and Systems Engineering

Citations in the corpus

Natural Compression for Distributed Deep Learning (2019, neutral): “Outside of the optimization for machine learning, compression operators are very relevant to optimal quantization theory and control theory (Elia and Mitter, 2001; Sun and Goyal, 2011; Sun et al., 2012).”
Network-Based Event-Triggered Control for Singular Systems With Quantizations (2016, methodology): “It was shown in [25] that the coarsest quantizer is logarithmic, and the sector bound method is applicable for stabilizing linear single-input–single-output systems with state quantization.”
Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks (2007, methodology): “This idea is an adaptation to the scheduling context of several techniques used to study problems of control under communication constraints [6], [11], [18].”
Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements (2013, methodology): “According to Elia and Mitter (2001) and Fu and Xie (2005), the logarithmic quantiser is given by”
Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses (2008, methodology): “The results presented in this section are obtained using standard techniques used to study sampled-data systems [48], [49], nonlinear systems subject to data losses [1], [21], [50], [22] and more recently scheduling of control tasks [51].”
Dither for smoothing relay feedback systems (2003, background): “Recently, there has been renewed interest due to a variety of emerging applications, such as automatic tuning of proportional integral derivative (PID) controllers [17], quantized control [18], and supervisory control [19].”
Control under communication constraints (2004, background): “Elia and Mitter examined the stabilizability problem in the case where the encoder is time invariant [7].”
Optimal LQG control across packet-dropping links (2007, positive): “Beginning with the seminal paper of Delchamps [5], quantization effects have been studied by Tatikonda [28], Elia and Mitter [6], Brockett and Liberzon [4], Hespanha et al.”
Quantized average consensus via dynamic coding/decoding schemes (2008, methodology): “Logarithmic coder This strategy is presented for example in [9].”
When bode meets shannon: control-oriented feedback communication schemes (2004, background): “There are two distinct avenues of research in this contest: One that studies how communication channels in the loop affect feedback performance (see [1]–[4], [6]–[8], and [10]–[12], to cite a few) and one that studies how feedback can affect communication schemes [16], [17], [13], [14], [26], [28]–[”
A new delay system approach to network-based control (2008, background): “(28) According to Elia and Mitter (2001), and Fu and Xie (2005), a quantizer is called logarithmic if the set of quantized levels is characterized by Uj = {±u(j)i , u(j)i = ij u(j)0 , i = ±1, ±2, . . .}, ∪{±u(j)0 } ∪ {0}, 0 < j < 1, u(j)0 > 0.”
Minimal data rate stabilization of nonlinear systems over networks with large delays (2007, positive): “[6], [2], [12], [7], [25], [27]) the problem has been very well understood, and an elegant characterization of the minimal data rate above which stabilization is always possible is now available.”
${\cal H}_{\infty}$ Estimation for Uncertain Systems With Limited Communication Capacity (2007, methodology): “For each , the set of quantized levels is described by (4) According to [5] and [7], a quantizer is called logarithmic if the set of quantized levels is characterized by (5) Each of the quantization level corresponds to a segment such that the quantizer maps the whole segment to this quantization…”