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Tingshu Hu - One of the best experts on this subject based on the ideXlab platform.

  • State-Space Approach to Modeling and Ripple Reduction in AC–DC Converters
    IEEE Transactions on Control Systems and Technology, 2013
    Co-Authors: Huong Pham, Hoeguk Jung, Tingshu Hu
    Abstract:

    This brief develops a systematic state-space approach to the modeling of boost type ac-dc converters and reduction of output ripples. The bridge rectifier voltage is modeled as a periodic disturbance whose harmonics have given frequencies but uncertain phases and magnitudes. These types of disturbances are described as the output of an external autonomous linear system, called an exogenous system, with uncertain initial conditions. The exogenous system is integrated with the boost converter as a whole system in state-space description. The whole system is a nonlinear system whose differential equation has a Bilinear Term, which is handled by an inclusion. Via a recently developed Lyapunov framework, the problem of ripple reduction is converted into a numerically efficient optimization problem, which involves linear matrix inequalities. The resulting feedback law is a simple linear state-feedback. Experimental results validate the effectiveness of the theoretical approach and design method.

  • ACC - Ripple reduction in AC-DC power converters via a Lyapunov approach
    2012 American Control Conference (ACC), 2012
    Co-Authors: Huong Pham, Hoeguk Jung, Tingshu Hu
    Abstract:

    This paper develops a systematic state-space approach to modeling of boost type AC-DC converters and reduction of the output ripples. The bridge rectifier voltage is modeled as a periodic disturbance whose harmonics have given frequencies but uncertain phases and magnitude. This type of disturbances are described as the output of an external autonomous linear system, called an exogenous system, with uncertain initial conditions. The exogenous system is integrated with the boost converter as a whole system in statespace description. The whole system is a nonlinear system whose differential equations has a Bilinear Term, which is handled by an inclusion. Via a recently developed Lyapunov framework, the problem of ripple reduction is converted into a numerically efficient optimization problem which involves linear matrix inequalities (LMIs). The resulting feedback law is a simple linear state-feedback. Experimental results validate the effectiveness of the theoretical approach and design method.

  • Stability and Robust Regulation of Battery-Driven Boost Converter With Simple Feedback
    IEEE Transactions on Power Electronics, 2011
    Co-Authors: Fidegnon Fassinou, Tingshu Hu
    Abstract:

    This paper investigates the problem of regulating the output voltage of a battery-driven boost converter, where the load is uncertain or changes within a certain range. The battery is modeled with a second order circuit with two capacitors. A state-space approach is developed for estimating the parameters of the battery. By using the state-space averaging method, the open-loop system for regulating the output voltage is described as a sixth order differential equation with a Bilinear Term and input constraints. A simple saturated state feedback is designed by solving some optimization problem with linear matrix inequality constraints. The optimized controller is very close to an integrator feedback. Using the newly developed Lyapunov method, we analyze the stability and regulation of the closed-loop Bilinear system including the battery dynamics. Computation shows that both the optimized state feedback and the integrator feedback can achieve practically global regulation in the presence of uncertain load and uncertain battery voltage. The results are validated by experimental systems. The effect of discontinuous conduction mode on transient response is discussed via simulation and experiment.

  • Stability and robust regulation of battery driven boost converter with simple feedback
    Proceedings of the 2011 American Control Conference, 2011
    Co-Authors: Fidegnon Fassinou, Tingshu Hu
    Abstract:

    This paper investigates the problem of regulating the output voltage of a battery driven boost converter where the load is uncertain or changes within a certain range. By using the state-space averaging method, the open-loop system for regulating the output voltage is described as a 6th order differential equation with a Bilinear Term and input constraints. A simple saturated state feedback is designed by solving some optimization problem with linear matrix inequality constraints. The optimized controller is very close to an integrator feedback. Using the newly developed Lyapunov method, we analyze the stability and regulation of the closed-loop Bilinear system. Computation shows that both the optimized state feedback and the integrator feedback can achieve practically global regulation in the presence of uncertain load and uncertain battery voltage. The results are validated by experimental systems.

  • a nonlinear system approach to analysis and design of power electronic converters with saturation and Bilinear Terms
    IEEE Transactions on Power Electronics, 2011
    Co-Authors: Tingshu Hu
    Abstract:

    Power-electronic converters are intrinsically nonlinear. This paper proposes a Lyapunov approach to analysis and design of a class of nonlinear systems arising from power-electronic converters. The system has a Bilinear Term as the product of the state and the input-the duty cycle, which is subject to strict constraint (or saturation). The nonlinearities and the input saturation are considered in this paper by using piecewise-quadratic Lyapunov functions and by describing the system with a piecewise-linear differential inclusion. The problems considered include controller design for robust stability, and estimation of stability region and tracking domain. These analysis and design problems are converted into numerically efficient optimization algorithms involving linear-matrix inequalities (LMIs). A buck-boost dc-dc converter is used to demonstrate the proposed methods. The optimization results show that a simple state-feedback law can be constructed to achieve practically global stabilization and tracking, which is theoretically confirmed by the Lyapunov approach. An experimental buck-boost converter is constructed to verify the tracking of a square reference varying almost between the upper and the lower limit.

Silviu Ciochina - One of the best experts on this subject based on the ideXlab platform.

  • A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms
    2019 International Symposium on Signals Circuits and Systems (ISSCS), 2019
    Co-Authors: Laura-maria Dogariu, Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu Ciochina
    Abstract:

    Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the system identification problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse Bilinear forms, in which the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the identification of such Bilinear forms. Experimental results highlight the good behavior of the proposed solution.

  • An Optimized LMS Algorithm for Bilinear Forms
    2018 International Symposium on Electronics and Telecommunications (ISETC), 2018
    Co-Authors: Laura-maria Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
    Abstract:

    In this paper we introduce an optimized version of the least-mean-square (LMS) algorithm, which is tailored for the particular case of Bilinear forms. In this context, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model resembling a multiple-input/single-output system. Moreover, a comparison with the simplified version of the Kalman filter developed for Bilinear forms is provided. Simulation results show the good performance of the proposed algorithm.

  • Regularized Recursive Least-Squares Algorithms for the Identification of Bilinear Forms
    2018 International Symposium on Electronics and Telecommunications (ISETC), 2018
    Co-Authors: Camelia Elisei-iliescu, Constantin Paleologu, Silviu Ciochina, Cristian Stanciu, Cristian Anghel, Jacob Benesty
    Abstract:

    The system identification problem is more challenging when the parameter space becomes large. This paper addresses the identification of Bilinear systems based on the regularized recursive least-squares algorithm. Here, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.

  • TSP - A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms
    2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018
    Co-Authors: Constantin Paleologu, Camelia Elisei-iliescu, Jacob Benesty, Cristian Stanciu, Cristian Anghel, Silviu Ciochina
    Abstract:

    Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of Bilinear forms, where the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such Bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in Terms of both convergence rate and computational complexity.

  • ICASSP - Identification of Bilinear Forms with the Kalman Filter
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Laura-maria Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
    Abstract:

    In this paper, we develop the Kalman filter for the identification of Bilinear forms. In this framework, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model, which resembles a multiple-input/single-output system. Recently, the identification of such Bilinear forms was addressed in Terms of the Wiener filter and conventional adaptive algorithms, i.e., least-mean-square and recursive least-squares. In this work, apart from the derivation of the Kalman filter tailored for the identification of Bilinear forms, a simplified (i.e., low complexity) version of the algorithm is also presented. Simulation results support the theoretical findings and indicate the good performance of the proposed solutions.

Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.

  • A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms
    2019 International Symposium on Signals Circuits and Systems (ISSCS), 2019
    Co-Authors: Laura-maria Dogariu, Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu Ciochina
    Abstract:

    Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the system identification problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse Bilinear forms, in which the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the identification of such Bilinear forms. Experimental results highlight the good behavior of the proposed solution.

  • An Optimized LMS Algorithm for Bilinear Forms
    2018 International Symposium on Electronics and Telecommunications (ISETC), 2018
    Co-Authors: Laura-maria Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
    Abstract:

    In this paper we introduce an optimized version of the least-mean-square (LMS) algorithm, which is tailored for the particular case of Bilinear forms. In this context, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model resembling a multiple-input/single-output system. Moreover, a comparison with the simplified version of the Kalman filter developed for Bilinear forms is provided. Simulation results show the good performance of the proposed algorithm.

  • Regularized Recursive Least-Squares Algorithms for the Identification of Bilinear Forms
    2018 International Symposium on Electronics and Telecommunications (ISETC), 2018
    Co-Authors: Camelia Elisei-iliescu, Constantin Paleologu, Silviu Ciochina, Cristian Stanciu, Cristian Anghel, Jacob Benesty
    Abstract:

    The system identification problem is more challenging when the parameter space becomes large. This paper addresses the identification of Bilinear systems based on the regularized recursive least-squares algorithm. Here, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.

  • TSP - A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms
    2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018
    Co-Authors: Constantin Paleologu, Camelia Elisei-iliescu, Jacob Benesty, Cristian Stanciu, Cristian Anghel, Silviu Ciochina
    Abstract:

    Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of Bilinear forms, where the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such Bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in Terms of both convergence rate and computational complexity.

  • ICASSP - Identification of Bilinear Forms with the Kalman Filter
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Laura-maria Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
    Abstract:

    In this paper, we develop the Kalman filter for the identification of Bilinear forms. In this framework, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model, which resembles a multiple-input/single-output system. Recently, the identification of such Bilinear forms was addressed in Terms of the Wiener filter and conventional adaptive algorithms, i.e., least-mean-square and recursive least-squares. In this work, apart from the derivation of the Kalman filter tailored for the identification of Bilinear forms, a simplified (i.e., low complexity) version of the algorithm is also presented. Simulation results support the theoretical findings and indicate the good performance of the proposed solutions.

Constantin Paleologu - One of the best experts on this subject based on the ideXlab platform.

  • A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms
    2019 International Symposium on Signals Circuits and Systems (ISSCS), 2019
    Co-Authors: Laura-maria Dogariu, Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu Ciochina
    Abstract:

    Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the system identification problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse Bilinear forms, in which the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the identification of such Bilinear forms. Experimental results highlight the good behavior of the proposed solution.

  • An Optimized LMS Algorithm for Bilinear Forms
    2018 International Symposium on Electronics and Telecommunications (ISETC), 2018
    Co-Authors: Laura-maria Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
    Abstract:

    In this paper we introduce an optimized version of the least-mean-square (LMS) algorithm, which is tailored for the particular case of Bilinear forms. In this context, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model resembling a multiple-input/single-output system. Moreover, a comparison with the simplified version of the Kalman filter developed for Bilinear forms is provided. Simulation results show the good performance of the proposed algorithm.

  • Regularized Recursive Least-Squares Algorithms for the Identification of Bilinear Forms
    2018 International Symposium on Electronics and Telecommunications (ISETC), 2018
    Co-Authors: Camelia Elisei-iliescu, Constantin Paleologu, Silviu Ciochina, Cristian Stanciu, Cristian Anghel, Jacob Benesty
    Abstract:

    The system identification problem is more challenging when the parameter space becomes large. This paper addresses the identification of Bilinear systems based on the regularized recursive least-squares algorithm. Here, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.

  • TSP - A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms
    2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018
    Co-Authors: Constantin Paleologu, Camelia Elisei-iliescu, Jacob Benesty, Cristian Stanciu, Cristian Anghel, Silviu Ciochina
    Abstract:

    Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of Bilinear forms, where the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such Bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in Terms of both convergence rate and computational complexity.

  • ICASSP - Identification of Bilinear Forms with the Kalman Filter
    2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018
    Co-Authors: Laura-maria Dogariu, Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Pablo Piantanida
    Abstract:

    In this paper, we develop the Kalman filter for the identification of Bilinear forms. In this framework, the Bilinear Term is defined with respect to the impulse responses of a spatiotemporal model, which resembles a multiple-input/single-output system. Recently, the identification of such Bilinear forms was addressed in Terms of the Wiener filter and conventional adaptive algorithms, i.e., least-mean-square and recursive least-squares. In this work, apart from the derivation of the Kalman filter tailored for the identification of Bilinear forms, a simplified (i.e., low complexity) version of the algorithm is also presented. Simulation results support the theoretical findings and indicate the good performance of the proposed solutions.

Qaisar Shafi - One of the best experts on this subject based on the ideXlab platform.

  • LHC constraints on NLSP gluino and dark matter neutralino in Yukawa unified models
    Physics Letters B, 2020
    Co-Authors: M. Adeel Ajaib, Tong Li, Qaisar Shafi
    Abstract:

    AbstractThe ATLAS experiment has recently presented its search results for final states containing jets and/or b-jet(s) and missing transverse momentum, corresponding to an integrated luminosity of 165 pb−1. We employ this data to constrain a class of supersymmetric SU(4)c×SU(2)L×SU(2)R models with t−b−τ Yukawa unification, in which the gluino is the next to lightest supersymmetric particle (NLSP). The NLSP gluino is slightly (∼10–30%) heavier than the LSP dark matter neutralino, and it primarily decays into the latter and a quark–antiquark pair or gluon. We find that NLSP gluino masses below ∼300 GeV are excluded by the ATLAS data. For LSP neutralino mass ∼200–300 GeV and μ>0, where μ is the coefficient of the MSSM Higgs Bilinear Term, the LHC constraints in some cases on the spin-dependent (spin-independent) neutralino–nucleon cross section are significantly more stringent than the expected bounds from IceCube DeepCore (Xenon 1T/SuperCDMS). For μ

  • LHC constraints on NLSP gluino and dark matter neutralino in Yukawa unified models
    Physics Letters B, 2011
    Co-Authors: M. Adeel Ajaib, Tong Li, Qaisar Shafi
    Abstract:

    Abstract The ATLAS experiment has recently presented its search results for final states containing jets and/or b -jet(s) and missing transverse momentum, corresponding to an integrated luminosity of 165 pb −1 . We employ this data to constrain a class of supersymmetric SU ( 4 ) c × SU ( 2 ) L × SU ( 2 ) R models with t − b − τ Yukawa unification, in which the gluino is the next to lightest supersymmetric particle (NLSP). The NLSP gluino is slightly (∼10–30%) heavier than the LSP dark matter neutralino, and it primarily decays into the latter and a quark–antiquark pair or gluon. We find that NLSP gluino masses below ∼300 GeV are excluded by the ATLAS data. For LSP neutralino mass ∼200–300 GeV and μ > 0 , where μ is the coefficient of the MSSM Higgs Bilinear Term, the LHC constraints in some cases on the spin-dependent (spin-independent) neutralino–nucleon cross section are significantly more stringent than the expected bounds from IceCube DeepCore (Xenon 1T/SuperCDMS). For μ 0 , this also holds for the spin-dependent cross sections.