The sector bound approach to quantized feedback control

By M. Fu, Lihua Xie. IEEE Transactions on Automatic Control, 2005

1,610 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a4f5530eb2b90dffbdb24cd0a24d8d9e3fec4d16

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In Control and Systems Engineering

1. Qing-Long Han
2. Huijun Gao
3. Xianming Zhang
4. Peng Shi
5. James Lam
6. Zidong Wang
7. Guanghong Yang
8. Tongwen Chen

Citations in the corpus

• Network-Based Event-Triggered Control for Singular Systems With Quantizations (2016, methodology): “For each f(·), the set of quantized levels is described as in [26] and [37] by”
• Distributed Consensus With Limited Communication Data Rate (2011, background): “It is well known that for feedback control of single-agent systems with communication constraints, Tatikonda and Mitter ([26]) and Nair and Evans ([27]) gave the minimal bit rate (channel capacity) for stabilizing a linear time-invariant system and the case with a logarithmic quantizer was considere”
• Sampling‐interval‐dependent stability for sampled‐data systems with state quantization (2014, methodology): “The robustness was analyzed in [22] for sampled-data systems with a logarithmic quantizer.”
• Networked control systems: a survey of trends and techniques (2020, background): “Essentially, a quantizer is a symmetrical piecewise function with respect to the origin, mapping input values to output values with an infinite countable number of levels [39].”
• Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey (2013, methodology): “Subsequently, by using the sector-bound approach, the quantized feedback control problems have been tackled in [23, 26] for linear discrete-time systems.”
• Survey on Recent Advances in Networked Control Systems (2016, background): “…Dropouts: When the network is in some worst conditions, such as overloaded network traffic and the occurrence of transmission time-outs and transmission errors in the communication network, packet dropouts may occur, even though network protocols are equipped with transmission retry mechanisms.”
• Network Topology and Communication Data Rate for Consensusability of Discrete-Time Multi-Agent Systems (2011, background): “Note that the intrinsic entropy rate determines the minimal data rate for stabilizing a networked con-trol system where a network is deployed to transmit data from sensor to controller and/or controller to actuator, see, e.g., [13], [29], [37], [43], [45], [48], to name a few.”
• Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements (2013, methodology): “qjð y jkÞ ¼ ð1þ D ð j Þ k Þ y j k with jD ð j Þ k j � �j, and the quantisation effects can then be transformed into sectorbounded uncertainties (Fu and Xie 2005).”
• Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links (2017, background): “As reported in [10] and [34]–[37], these phenomena, such as packet dropouts, signal quantization, actuator/sensor saturation, random communication time delays, and so forth, could degrade the performance of NCSs significantly.”
• Adaptive asymptotic tracking control of uncertain nonlinear systems with input quantization and actuator faults (2016, background): “Lots of well known results on quantized control have been published in Brockett and Liberzon (2000), Elia and Mitter (2001), Fu and Xie (2005), Liberzon (2003), Liu, Jiang, and Hill (2012) and Lunze, Nixdorf, and Schroder (1999).”
• 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.”
• ${\cal H}_{\infty}$ 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…”
• Robust control for a class of uncertain nonlinear systems with input quantization (2016, methodology): “In [7], the coarsest quantizer was taken as a sector-bound uncertainty to study quantized control with quadratic stability and developed H2 and H1 performance criteria.”