The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Vadim I. Utkin - One of the best experts on this subject based on the ideXlab platform.
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Design of a Continuous Signal Generator Based on Sliding Mode Control of Three-Phase AC-DC Power Converters
Energies, 2019Co-Authors: Yazan M. Alsmadi, Isaac Chairez, Vadim I. UtkinAbstract:In recent years, hundreds of technical papers have been published which describe the use of sliding mode control (SMC) techniques for power electronic equipment and electrical drives. SMC with disContinuous control actions has the potential to circumvent parameter variation effects with low implementation complexity. The problem of controlling time-varying DC loads has been studied in literature if three-phase input voltage sources are available. The conventional approach implies the design of a three-phase AC/DC converter with a constant output voltage. Then, an additional DC/DC converter is utilized as an additional stage in the output of the converter to generate the required voltage for the load. A controllable AC/DC converter is always used to have a high quality of the consumed power. The aim of this study is to design a controlled Continuous Signal generator based on the sliding mode control of a three-phase AC-DC power converter, which yields the production of Continuous variations of the output DC voltage. A sliding mode current tracking system is designed with reference phase currents proportional to the source voltage. The proportionality time-varying gain is selected such that the output voltage is equal to the desired time function. The proposed new topology also offers the capability to get rid of the additional DC/DC power converter and produces the desired time-varying control function in the output of AC/DC power converter. The effectiveness of the proposed control design is demonstrated through a wide range of MATLAB/Simulink simulations.
Damir Sersic - One of the best experts on this subject based on the ideXlab platform.
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sampling and reconstruction of sparse Signals in shift invariant spaces generalized shannon s theorem meets compressive sensing
arXiv: Signal Processing, 2020Co-Authors: Tin Vlasic, Damir SersicAbstract:This paper introduces a novel framework and corresponding methods for sampling and reconstruction of sparse Signals in shift-invariant (SI) spaces. We reinterpret the random demodulator, a system that acquires sparse bandlimited Signals, as a system for acquisition of linear combinations of the samples in the SI setting with the box function as the sampling kernel. The sparsity assumption is exploited by compressive sensing (CS) framework for recovery of the SI samples from a reduced set of measurements. The samples are subsequently filtered by a discrete-time correction filter in order to reconstruct expansion coefficients of an observed Signal. Furthermore, we offer a generalization of the proposed framework to other sampling kernels that lie in arbitrary SI spaces. The generalized method embeds the correction filter in a CS optimization problem which directly reconstructs expansion coefficients of the Signal. Both approaches recast an inherently infinite-dimensional inverse problem as a finite-dimensional CS problem in an exact way. Finally, we conduct numerical experiments on Signals in B-spline spaces whose expansion coefficients are assumed to be sparse in a certain transform domain. The coefficients can be regarded as parametric models of an underlying Continuous Signal, obtained from a reduced set of measurements. Such Continuous Signal representations are particularly suitable for Signal processing without converting them into samples.
Yazan M. Alsmadi - One of the best experts on this subject based on the ideXlab platform.
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Design of a Continuous Signal Generator Based on Sliding Mode Control of Three-Phase AC-DC Power Converters
Energies, 2019Co-Authors: Yazan M. Alsmadi, Isaac Chairez, Vadim I. UtkinAbstract:In recent years, hundreds of technical papers have been published which describe the use of sliding mode control (SMC) techniques for power electronic equipment and electrical drives. SMC with disContinuous control actions has the potential to circumvent parameter variation effects with low implementation complexity. The problem of controlling time-varying DC loads has been studied in literature if three-phase input voltage sources are available. The conventional approach implies the design of a three-phase AC/DC converter with a constant output voltage. Then, an additional DC/DC converter is utilized as an additional stage in the output of the converter to generate the required voltage for the load. A controllable AC/DC converter is always used to have a high quality of the consumed power. The aim of this study is to design a controlled Continuous Signal generator based on the sliding mode control of a three-phase AC-DC power converter, which yields the production of Continuous variations of the output DC voltage. A sliding mode current tracking system is designed with reference phase currents proportional to the source voltage. The proportionality time-varying gain is selected such that the output voltage is equal to the desired time function. The proposed new topology also offers the capability to get rid of the additional DC/DC power converter and produces the desired time-varying control function in the output of AC/DC power converter. The effectiveness of the proposed control design is demonstrated through a wide range of MATLAB/Simulink simulations.
Xin Peng - One of the best experts on this subject based on the ideXlab platform.
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ICNC (7) - Spectra Analysis of Sampling and Reconstructing Continuous Signal Using Hamming Window Function
2008 Fourth International Conference on Natural Computation, 2008Co-Authors: Yizhong Song, Xin PengAbstract:Hamming window function was applied to studying sampling theorem. A Continuous band-limited spectrum function F(w) was constructed with Hamming window function. Its corresponding time-domain Signal f(t) was worked out by inverse Fourier transform. f(t) was sampled with a comb function dT(t). By modifying the value of T, all kinds of sampling Signals were produced, including critical, over and under sampling. With FFT, the frequency spectrum of each sampling Signal was figured out. Each spectrum profile was analyzed. The process to reconstruct f(t) was suggested, and the reconstructed results from each of the three kinds of sampling Signals were discussed. As the result, critical sampling frequency spectrum in FFT principal value sequence was aliasing at the middle point, over sampling's disconnected, and under sampling's overlapped. The original Signal could be accurately reconstructed from over samplings, but couldn't from under one. Hamming window is a perfect model for analyzing and demonstrating the sampling theorem.
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Spectra Analysis of Sampling and Reconstructing Continuous Signal Using Hamming Window Function
2008 Fourth International Conference on Natural Computation, 2008Co-Authors: Yizhong Song, Xin PengAbstract:Hamming window function was applied to studying sampling theorem. A Continuous band-limited spectrum function F(w) was constructed with Hamming window function. Its corresponding time-domain Signal f(t) was worked out by inverse Fourier transform. f(t) was sampled with a comb function dT(t). By modifying the value of T, all kinds of sampling Signals were produced, including critical, over and under sampling. With FFT, the frequency spectrum of each sampling Signal was figured out. Each spectrum profile was analyzed. The process to reconstruct f(t) was suggested, and the reconstructed results from each of the three kinds of sampling Signals were discussed. As the result, critical sampling frequency spectrum in FFT principal value sequence was aliasing at the middle point, over sampling's disconnected, and under sampling's overlapped. The original Signal could be accurately reconstructed from over samplings, but couldn't from under one. Hamming window is a perfect model for analyzing and demonstrating the sampling theorem.
Tin Vlasic - One of the best experts on this subject based on the ideXlab platform.
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sampling and reconstruction of sparse Signals in shift invariant spaces generalized shannon s theorem meets compressive sensing
arXiv: Signal Processing, 2020Co-Authors: Tin Vlasic, Damir SersicAbstract:This paper introduces a novel framework and corresponding methods for sampling and reconstruction of sparse Signals in shift-invariant (SI) spaces. We reinterpret the random demodulator, a system that acquires sparse bandlimited Signals, as a system for acquisition of linear combinations of the samples in the SI setting with the box function as the sampling kernel. The sparsity assumption is exploited by compressive sensing (CS) framework for recovery of the SI samples from a reduced set of measurements. The samples are subsequently filtered by a discrete-time correction filter in order to reconstruct expansion coefficients of an observed Signal. Furthermore, we offer a generalization of the proposed framework to other sampling kernels that lie in arbitrary SI spaces. The generalized method embeds the correction filter in a CS optimization problem which directly reconstructs expansion coefficients of the Signal. Both approaches recast an inherently infinite-dimensional inverse problem as a finite-dimensional CS problem in an exact way. Finally, we conduct numerical experiments on Signals in B-spline spaces whose expansion coefficients are assumed to be sparse in a certain transform domain. The coefficients can be regarded as parametric models of an underlying Continuous Signal, obtained from a reduced set of measurements. Such Continuous Signal representations are particularly suitable for Signal processing without converting them into samples.