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Journal articles on the topic 'Quantized input'

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Author: Grafiati

Published: 14 March 2023

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1

Mena-Parra, J., K. Bandura, M. A. Dobbs, J. R. Shaw, and S. Siegel. "Quantization Bias for Digital Correlators." Journal of Astronomical Instrumentation 07, no. 02n03 (September 2018): 1850008. http://dx.doi.org/10.1142/s2251171718500083.

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In radio interferometry, the quantization process introduces a bias in the magnitude and phase of the measured correlations which translates into errors in the measurement of source brightness and position in the sky, affecting both the system calibration and image reconstruction. In this paper, we investigate the biasing effect of quantization in the measured correlation between complex-valued inputs with a circularly symmetric Gaussian probability density function (PDF), which is the typical case for radio astronomy applications. We start by calculating the correlation between the input and quantization error and its effect on the quantized variance, first in the case of a real-valued quantizer with a zero mean Gaussian input and then in the case of a complex-valued quantizer with a circularly symmetric Gaussian input. We demonstrate that this input-error correlation is always negative for a quantizer with an odd number of levels, while for an even number of levels, this correlation is positive in the low signal level regime. In both cases, there is an optimal interval for the input signal level for which this input-error correlation is very weak and the model of additive uncorrelated quantization noise provides a very accurate approximation. We determine the conditions under which the magnitude and phase of the measured correlation have negligible bias with respect to the unquantized values: we demonstrate that the magnitude bias is negligible only if both unquantized inputs are optimally quantized (i.e. when the uncorrelated quantization error model is valid), while the phase bias is negligible when (1) at least one of the inputs is optimally quantized, or when (2) the correlation coefficient between the unquantized inputs is small. Finally, we determine the implications of these results for radio interferometry.

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2

Xia, Xiaonan, Yu Fang, and Tianping Zhang. "Adaptive quantized DSC of output-constrained uncertain nonlinear systems with quantized input and input unmodeled dynamics." Journal of the Franklin Institute 357, no. 9 (June 2020): 5199–225. http://dx.doi.org/10.1016/j.jfranklin.2020.02.042.

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3

Zanma, Tadanao, Makoto Azegami, and Kang-Zhi Liu. "Optimal Input and Quantization Interval for Quantized Feedback System With Variable Quantizer." IEEE Transactions on Industrial Electronics 64, no. 3 (March 2017): 2246–54. http://dx.doi.org/10.1109/tie.2016.2625240.

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4

TORIKAI, HIROYUKI, TOSHIMICHI SAITO, and YOSHINOBU KAWASAKI. "ANALYSIS OF A QUANTIZED CHAOTIC SYSTEM." International Journal of Bifurcation and Chaos 12, no. 05 (May 2002): 1207–18. http://dx.doi.org/10.1142/s0218127402005054.

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We consider quantized chaotic dynamics for a spiking oscillator with two periodic inputs. As the first input is applied, the oscillator generates various periodic and chaotic pulse-trains governed by a pulse position map. As the second input is added, the oscillator produces pulse positions restricted on a lattice, and the pulse position map is quantized. Then the oscillator generates a set of super-stable periodic pulse-trains (SSPTs). The oscillator has various coexisting SSPTs and generates one of them depending on the initial state condition. In order to characterize the set of SSPTs, we elucidate the number and the minimum pulse interval of the SSPTs theoretically. By presenting a simple test circuit, we then verify some typical phenomena in the laboratory environment.

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5

Coutinho, D. F., Minyue Fu, and C. E. de Souza. "Input and Output Quantized Feedback Linear Systems." IEEE Transactions on Automatic Control 55, no. 3 (March 2010): 761–66. http://dx.doi.org/10.1109/tac.2010.2040497.

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6

Chitour, Y., A. Marigo, and B. Piccoli. "Time optimal control for quantized input systems." IFAC Proceedings Volumes 37, no. 13 (September 2004): 1009–14. http://dx.doi.org/10.1016/s1474-6670(17)31358-7.

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7

Marigo, Alessia. "Optimal input sets for steering quantized systems." Mathematics of Control, Signals, and Systems 22, no. 2 (September 5, 2010): 129–53. http://dx.doi.org/10.1007/s00498-010-0055-2.

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8

Danielson, Claus, and Stefano Di Cairano. "Robust Soft-Landing Control with Quantized Input." IFAC-PapersOnLine 49, no. 18 (2016): 35–40. http://dx.doi.org/10.1016/j.ifacol.2016.10.136.

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9

Maestrelli, Rafael, Daniel Coutinho, and Carlos E. de Souza. "Input and Output Finite-Level Quantized Linear Control Systems: Stability Analysis and Quantizer Design." Journal of Control, Automation and Electrical Systems 26, no. 2 (January 13, 2015): 105–14. http://dx.doi.org/10.1007/s40313-014-0163-1.

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10

Ji, Mingming, and Shengchao Su. "Identification of Rational Systems with Logarithmic Quantized Data." Mathematical Problems in Engineering 2021 (July 13, 2021): 1–7. http://dx.doi.org/10.1155/2021/6667745.

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This paper is concerned with the quantized identification of rational systems, where the systems’ output is quantized by a logarithmic quantizer. Under the assumption that the systems’ input is periodic, the identification procedure is categorized into two steps. The first step is to identify the noise-free output of systems based on the quantized data. The second is to identify the unknown parameter based on the input and the estimation of the noise-free output. The identification algorithm is also summarized. Asymptotic convergence of the estimators is analyzed in detail, which shows that the estimators are convergent almost everywhere. A numerical example is given to illustrate the results obtained in this paper.

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11

Shi, Huaitao, Maxiao Hou, and Yuhou Wu. "Distributed Control for Leader-Following Consensus Problem of Second-Order Multi-Agent Systems and Its Application to Motion Synchronization." Applied Sciences 9, no. 20 (October 9, 2019): 4208. http://dx.doi.org/10.3390/app9204208.

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This paper solves the leader-following consensus problem for a class of second-order multi-agent systems with input quantized by a newly proposed adaptive dynamic quantizer. The novel dynamic quantizer is an adaptive quantizer that combines the logarithmic quantizer and the uniform quantizer by introducing dynamic gain parameters to achieve quantizer adaptive adjustment. It has advantages of logarithmic, uniform, and adaptive dynamic quantizers in ensuring reducible communication expenses and acceptable quantizer errors for better system performance. On this basis, we transform the guide way climbing frame (GWCF) under ideal conditions into a second-order multi-agent system and solve the motion synchronization problem of GWCF. Finally, we illustrate our approach by numerical examples.

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12

Kliewer, J., and A. Mertins. "Soft-Input Reconstruction of Binary Transmitted Quantized Overcomplete Expansions." IEEE Signal Processing Letters 11, no. 11 (November 2004): 899–903. http://dx.doi.org/10.1109/lsp.2004.836941.

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13

Yoo, Sung Jin. "Adaptive State-Quantized Control of Uncertain Lower-Triangular Nonlinear Systems with Input Delay." Mathematics 9, no. 7 (April 1, 2021): 763. http://dx.doi.org/10.3390/math9070763.

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In this paper, we investigate the adaptive state-quantized control problem of uncertain lower-triangular systems with input delay. It is assumed that all state variables are quantized for the feedback control design. The error transformation method using an auxiliary time-varying signal is presented to deal with the compensation problem of input delay. Based on the error surfaces with the auxiliary variable, a neural-network-based adaptive state-quantized control scheme is constructed with the design of the input delay compensator. Different from existing results in the literature, the proposed method exhibits the following features: (i) compensating for the input delay effect by using quantized states; and (ii) establishing the stability of the adaptive quantized feedback control system in the presence of input delay. Furthermore, the boundedness of all the signals in the closed-loop and the convergence of the tracking error are analyzed. The effectiveness of the developed control strategy is demonstrated through the simulation on a hydraulic servo system.

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14

Guay, Martin, and Daniel J. Burns. "Extremum Seeking Control for Discrete-Time with Quantized and Saturated Actuators." Processes 7, no. 11 (November 8, 2019): 831. http://dx.doi.org/10.3390/pr7110831.

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This paper proposes an extremum-seeking controller (ESC) design for a class of discrete-time nonlinear control systems subject to input constraints or quantized inputs. The proposed method implements a proportional-integral ESC design along with a discrete-time anti-windup mechanism. The anti-windup enforces input saturation while preserving the input dither signal. The technique incorporates a mechanism for adjusting the amplitude of the extremum seeking control dither signal. This mechanism ensures that any violation of constraints due to the dither signal is removed while maintaining the probing signal active. An amplitude update routine is also proposed. The amplitude update is coupled with a saturation bias estimation algorithm that correctly accounts for the inherent bias associated with systems operated at or near saturation conditions. The amplitude update is designed to remove the dither signal when the system approaches the optimum. It also ensures that a lower bound of the amplitude is enforced to guarantee that excitation conditions are maintained.

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15

Liu, Ruixia, Ming Liu, Xibin Cao, and Yuan Liu. "Optimal sliding mode tracking control of spacecraft formation flying with limited data communication." Advances in Mechanical Engineering 10, no. 6 (June 2018): 168781401878204. http://dx.doi.org/10.1177/1687814018782048.

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This article deals with the optimal tracking control problem for spacecraft formation flying via a sliding mode approach in the presence of external disturbances and signal quantization, where both state quantization and input quantization are considered. First, the Gauss pseudospectral method is adopted to solve the multi-objective optimization problem, where performance optimization, thruster amplitude constraints, and collision avoidance are simultaneously taken into consideration. Second, a novel quantized sliding mode control strategy is developed by employing a dynamic logarithmic quantizer to track the obtained optimal trajectories of relative position and velocity. In this design, the quantizer parameters are input into the designed controller to compensate for the signal quantization effects. Under the proposed robust quantized sliding mode control strategy, the resulting closed-loop control system is asymptotically stable with satisfying performance multi-objective constraints. Finally, a simulation example is presented to show the effectiveness of the proposed control design scheme.

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16

Yuan, Jiaxin, and Tao Chen. "Switched Fractional Order Multiagent Systems Containment Control with Event-Triggered Mechanism and Input Quantization." Fractal and Fractional 6, no. 2 (January 31, 2022): 77. http://dx.doi.org/10.3390/fractalfract6020077.

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This paper studies the containment control problem for a class of fractional order nonlinear multiagent systems in the presence of arbitrary switchings, unmeasured states, and quantized input signals by a hysteresis quantizer. Under the framework of the Lyapunov function theory, this paper proposes an event-triggered adaptive neural network dynamic surface quantized controller, in which dynamic surface control technology can avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously. Radial basis function neural networks (RBFNNs) are used to approximate the unknown nonlinear functions, and an observer is designed to obtain the unmeasured states. The proposed distributed protocol can ensure all the signals remain semi-global uniformly ultimately bounded in the closed-loop system, and all followers can converge to the convex hull spanned by the leaders’ trajectory. Utilizing the combination of an event-triggered scheme and quantized control technology, the controller is updated aperiodically only at the event-sampled instants such that transmitting and computational costs are greatly reduced. Simulations compare the event-triggered scheme without quantization control technology with the control method proposed in this paper, and the results show that the event-triggered scheme combined with the quantization mechanism reduces the number of control inputs by 7% to 20%.

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17

Filatov, Vladimir I., Alexander S. Nekrasov, Irina A. Rudzit, and Daria A. Kondrashova. "WEIGHTLESS PROCESSING OF QUANTIZED SIGNAL LOAD." T-Comm 15, no. 1 (2021): 48–51. http://dx.doi.org/10.36724/2072-8735-2021-15-1-48-51.

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Optimal methods for processing input information signals often involve operations, implementation of which is extremely difficult and significantly increases the requirements for automated information processing systems. However, the use of various approaches to solving this problem has led to the appearance of synthesized methods for processing a sequence of signals that allow solving the detection problem with the required quality without significant hardware complications. The article considers a method for weightless processing packets of input quantized signals, which allows us to evaluate the potential (limit) quality of information processing and quantify the amount of loss of this quality when excluding certain operations. The considered method is given with a reasonable structure of implemented devices in practice. A special feature of weightless signal processing is analysis of increasing unit density in a fixed interval of close positions, which gives information about the possible presence of an information signal. To identify this factor, two logical criteria are used, such as “m out of m” and “n out of m”, which will be described in this article.

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18

Ren, Wei, and Junlin Xiong. "Input-to-State Stability of Networked and Quantized Control Systems." IFAC-PapersOnLine 53, no. 2 (2020): 3079–84. http://dx.doi.org/10.1016/j.ifacol.2020.12.1016.

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19

Kameneva, Tania, and Dragan Nešić. "Input-to-State Stabilization of Nonlinear Systems with Quantized Feedback." IFAC Proceedings Volumes 41, no. 2 (2008): 12480–85. http://dx.doi.org/10.3182/20080706-5-kr-1001.02112.

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20

Marigo, Alessia. "Optimal input sets for time minimality in quantized control systems." Mathematics of Control, Signals, and Systems 18, no. 2 (July 25, 2005): 101–46. http://dx.doi.org/10.1007/s00498-005-0156-5.

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21

Gu, Guoxiang, Shuang Wan, and Li Qiu. "Networked stabilization for multi-input systems over quantized fading channels." Automatica 61 (November 2015): 1–8. http://dx.doi.org/10.1016/j.automatica.2015.07.019.

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22

Zhou, Jing, Changyun Wen, and Wei Wang. "Adaptive control of uncertain nonlinear systems with quantized input signal." Automatica 95 (September 2018): 152–62. http://dx.doi.org/10.1016/j.automatica.2018.05.014.

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23

Song, Gongfei, Tao Li, Kai Hu, and Bo-Chao Zheng. "Observer-based quantized control of nonlinear systems with input saturation." Nonlinear Dynamics 86, no. 2 (July 21, 2016): 1157–69. http://dx.doi.org/10.1007/s11071-016-2954-3.

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24

Casini, Marco, Andrea Garulli, and Antonio Vicino. "Input design in worst-case system identification with quantized measurements." Automatica 48, no. 12 (December 2012): 2997–3007. http://dx.doi.org/10.1016/j.automatica.2012.08.016.

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25

Farahani, Mozhgan A., Alireza Vahid, and Allison E. Goodwell. "Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach." Entropy 24, no. 7 (July 18, 2022): 994. http://dx.doi.org/10.3390/e24070994.

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Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.

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26

Chen, Hongmei, Li Wang, Ting Li, Lin He, and Fujiang Lin. "A 0.6V 19.5μW 80dB DR ΔΣ Modulator with SA-Quantizers and Digital Feedforward Path." Journal of Circuits, Systems and Computers 26, no. 07 (March 17, 2017): 1750117. http://dx.doi.org/10.1142/s0218126617501171.

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This paper presents a discrete-time multi-bit Delta–Sigma modulator employing successive approximation (SA)-quantizers for bio-signal acquisitions. In the proposed [Formula: see text] modulator, the input signal is separately quantized and the signal summation is performed in the digital domain to avoid the power hungry analog adder. Two SA-quantizers are used in this modulator. One is dedicated to quantize the input signal and the other is to quantize the summation of the integrators’ outputs. Dynamic Element Matching (DEM) technique is used to mitigate the mismatch among the digital-to-analog conversion (DAC) elements. To reduce the complexity of the DEM logic, the 7-bit summed quantizer output is truncated into a 5-bit code before it is fed to the DEM circuits. Double tailed inverter-based op-amp is used in the loop filter for low-voltage operation. Correlated-double-sampling is adopted to enhance the effective gain of the integrator. The proposed modulator is designed and fabricated in a 130-nm CMOS technology. The measurement result shows that the modulator achieves a dynamic range of 80[Formula: see text]dB, a peak SNDR of 77[Formula: see text]dB in a 25[Formula: see text]kHz signal bandwidth at sampling rate of 800[Formula: see text]kHz. The prototype modulator occupies 0.25[Formula: see text]mm2 and consumes only 19.5[Formula: see text][Formula: see text]W from a 0.6[Formula: see text]V supply. The proposed modulator achieves a figure of merit of 67 fJ per conversion step.

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27

Jiang, Yan, and Junyong Zhai. "Global sampled-data output feedback stabilization for a class of switched stochastic nonlinear systems with quantized input and unknown output gain." Transactions of the Institute of Measurement and Control 41, no. 16 (July 19, 2019): 4511–20. http://dx.doi.org/10.1177/0142331219862976.

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This paper aims at addressing the sampled-data output feedback control problem for a class of uncertain switched stochastic nonlinear systems, whose control input is quantized by a logarithmic quantizer and the output gain cannot be precisely known. We design a compensator with the quantized information. With the help of the feedback domination approach and the backstepping design method, a sampled-data output feedback controller is constructed with appropriate design parameters and a maximum sampling period to guarantee the global exponential stability in mean square of the closed-loop system under arbitrary switching. Finally, a numerical example is given to illustrate the effectiveness of the proposed scheme.

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28

Cao, Fangfei, and Jinkun Liu. "Boundary vibration control for a two-link rigid–flexible manipulator with quantized input." Journal of Vibration and Control 25, no. 23-24 (September 5, 2019): 2935–45. http://dx.doi.org/10.1177/1077546319873507.

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The aim of this paper is to investigate the control problem for a two-link rigid–flexible manipulator with input quantization. Abundant studies of the quantized control problem are based on the lumped parameter system, which is expressed using ordinary differential equations. In this paper, the dynamic model for a two-link rigid–flexible manipulator is represented using partial differential equations. The controller design and analysis with input quantization are based on the partial differential equation model. By means of an auxiliary system, tracking errors and boundary vibrations can be eliminated. Simulations are given to verify the effectiveness of the proposed controller with quantized input.

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29

Casini, Marco, Andrea Garulli, and Antonio Vicino. "Input design for worst-case system identification with uniformly quantized measurements." IFAC Proceedings Volumes 42, no. 10 (2009): 54–59. http://dx.doi.org/10.3182/20090706-3-fr-2004.00008.

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30

Zhou, Jing, Changyun Wen, and Guanghong Yang. "Adaptive Backstepping Stabilization of Nonlinear Uncertain Systems With Quantized Input Signal." IEEE Transactions on Automatic Control 59, no. 2 (February 2014): 460–64. http://dx.doi.org/10.1109/tac.2013.2270870.

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31

Richter, H., and E. A. Misawa. "Stability of discrete-time systems with quantized input and state measurements." IEEE Transactions on Automatic Control 48, no. 8 (August 2003): 1453–58. http://dx.doi.org/10.1109/tac.2003.815044.

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32

Liberzon, Daniel, and Dragan Nesic. "Input-to-State Stabilization of Linear Systems With Quantized State Measurements." IEEE Transactions on Automatic Control 52, no. 5 (May 2007): 767–81. http://dx.doi.org/10.1109/tac.2007.895850.

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33

Cepeda, Alfonso, and Alessandro Astolfi. "Control of a planar system with quantized and saturated input/output." IEEE Transactions on Circuits and Systems I: Regular Papers 55, no. 3 (April 2008): 932–42. http://dx.doi.org/10.1109/tcsi.2008.916448.

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34

Wang, Fujie, Zhi Liu, and Yun Zhang. "Adaptive bilateral control of teleoperators with actuator uncertainty and quantized input." Advances in Mechanical Engineering 9, no. 12 (December 2017): 168781401773955. http://dx.doi.org/10.1177/1687814017739550.

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35

Wang, Fujie, Zhi Liu, Yun Zhang, and C. L. Philip Chen. "Adaptive fuzzy visual tracking control for manipulator with quantized saturation input." Nonlinear Dynamics 89, no. 2 (May 5, 2017): 1241–58. http://dx.doi.org/10.1007/s11071-017-3513-2.

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36

Prior, Gideon, and Miroslav Krstic. "Quantized-Input Control Lyapunov Approach for Permanent Magnet Synchronous Motor Drives." IEEE Transactions on Control Systems Technology 21, no. 5 (September 2013): 1784–94. http://dx.doi.org/10.1109/tcst.2012.2212246.

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37

Yang, Zhichun, and Yiguang Hong. "Stabilization of impulsive hybrid systems using quantized input and output feedback." Asian Journal of Control 14, no. 3 (April 6, 2011): 679–92. http://dx.doi.org/10.1002/asjc.385.

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38

Chen, Jiayu, and Qiang Ling. "Robust quantized consensus of discrete multi-agent systems under input saturation." Journal of the Franklin Institute 356, no. 5 (March 2019): 2934–59. http://dx.doi.org/10.1016/j.jfranklin.2018.11.033.

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39

Xing, Xueyan, Hongjun Yang, Jinkun Liu, and Shuquan Wang. "Vibration control of nonlinear three-dimensional length-varying string with input quantization." Journal of Vibration and Control 26, no. 19-20 (February 12, 2020): 1835–47. http://dx.doi.org/10.1177/1077546320907762.

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This article studies the stability problem for a three-dimensional string with variable length in the case of input quantization. A nonlinear partial differential equation model is used to depict the dynamic characteristics of the length-varying flexible string with distributed variable parameters. The control signals are effectively mapped from a continuous region to a discrete set of numerical signals before being transmitted through communication channels using quantizers. With no information about quantizers, the vibration of the string is eliminated under the proposed adaptive quantized control despite of the actuator degradation, and the stability of the closed-loop system is demonstrated based on the Lyapunov’s direct method. Simulation results are supplied to show the effectiveness of the presented control strategy.

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40

Wang, Huanqing, Siwen Liu, Ding Wang, Ben Niu, and Ming Chen. "Adaptive neural tracking control of high-order nonlinear systems with quantized input." Neurocomputing 456 (October 2021): 156–67. http://dx.doi.org/10.1016/j.neucom.2021.05.054.

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41

Li, Yun, and Fan Yang. "Robust adaptive attitude control for non-rigid spacecraft with quantized control input." IEEE/CAA Journal of Automatica Sinica 7, no. 2 (March 2020): 472–81. http://dx.doi.org/10.1109/jas.2020.1003000.

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42

Li, Can, and Jie Lian. "Event-triggered feedback stabilization of switched linear systems using dynamic quantized input." Nonlinear Analysis: Hybrid Systems 31 (February 2019): 292–301. http://dx.doi.org/10.1016/j.nahs.2018.10.003.

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43

Chang, Xiao-Heng, Zhi-Min Li, Jun Xiong, and Yi-Ming Wang. "LMI approaches to input and output quantized feedback stabilization of linear systems." Applied Mathematics and Computation 315 (December 2017): 162–75. http://dx.doi.org/10.1016/j.amc.2017.07.038.

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44

Song, Gongfei, Hao Shen, Yunliang Wei, and Ze Li. "Quantized Output Feedback Control of Uncertain Discrete-Time Systems with Input Saturation." Circuits, Systems, and Signal Processing 33, no. 10 (April 24, 2014): 3065–83. http://dx.doi.org/10.1007/s00034-014-9795-4.

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45

Xing, Lantao, Changyun Wen, Zhitao Liu, Guanyu Lai, and Hongye Su. "Robust adaptive output feedback control for uncertain nonlinear systems with quantized input." International Journal of Robust and Nonlinear Control 27, no. 11 (September 21, 2016): 1999–2016. http://dx.doi.org/10.1002/rnc.3669.

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46

Liu, Qing Quan. "Observer-Based Quantized Feedback Control via Noisy Communication Channels." Advanced Materials Research 433-440 (January 2012): 6242–49. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6242.

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This paper investigates the input and output quantized control problem for stochastic linear systems with unbounded and possibly non-Gaussian process disturbance, where sensors, controllers and plants are connected by a noisy digital communication channel. Due to the unbounded process disturbance, a dynamic, logarithmic quantization scheme is proposed. An observer-based control policy is presented to stabilize the unstable plant in the mean square sense. Simulation results show the validity of the proposed quantization and control policy.

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47

Liu, Wenhui, Qian Ma, Shengyuan Xu, and Zhengqiang Zhang. "Adaptive finite‐time event‐triggered control for nonlinear systems with quantized input signals." International Journal of Robust and Nonlinear Control 31, no. 10 (March 30, 2021): 4764–81. http://dx.doi.org/10.1002/rnc.5510.

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48

Lee, Won Il, and Bum Yong Park. "Stabilization of Markovian Jump Systems With Quantized Input and Generally Uncertain Transition Rates." IEEE Access 9 (2021): 83499–506. http://dx.doi.org/10.1109/access.2021.3086504.

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49

Wu, Jian, Zheng-Guang Wu, Jing Li, Guangjun Wang, Haiying Zhao, and Weisheng Chen. "Practical Adaptive Fuzzy Control of Nonlinear Pure-Feedback Systems With Quantized Nonlinearity Input." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 3 (March 2019): 638–48. http://dx.doi.org/10.1109/tsmc.2018.2800783.

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50

Cerone, V., D. Piga, and D. Regruto. "Fixed-order FIR approximation of linear systems from quantized input and output data." Systems & Control Letters 62, no. 12 (December 2013): 1136–42. http://dx.doi.org/10.1016/j.sysconle.2013.09.012.

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