Showing papers on "Transfer function published in 2020"
TL;DR: In this paper, the Sumudu transform has been used to construct new transfer functions that will lead to new Bode, Nichols and Nyquist plots, and the question that arises in the work, is the following: Can we apply the SUMUDU transform to construct a new transfer function that can be used in signal analysis, including the Bode diagram, Nyquist plot and Nichols plot?
Abstract: In the last past year researchers have relied on the ability of Laplace transform to solve partial, ordinary linear equations with great success. Important analysis in signal analysis including the transfer function, Bode diagram, Nyquist plot and Nichols plot are obtained based on the Laplace transform. The output of the analysis depends only on the results obtained from Laplace transform. However, one weakness of Laplace transform is that the Laplace transform of even function is odd while the Laplace transform of an old function is even which is lack of conservation of properties. On the other hand there exist a similar integral transform known as Sumudu transform has the ability to conserve the properties of the function from real space to complex space. The question that arises in the work, is the following: Can we apply the Sumudu transform to construct new transfer functions that will lead to new Bode, Nichols and Nyquist plots? this question is answered in this work.
93 citations
TL;DR: A reduced-order small-signal closed-loop transfer function model based on Jordan continued-fraction expansion is proposed to assess the dynamic characteristics of the droop-controlled inverter and provide the preprocessing method for the real-time simulation of power systems.
Abstract: This article proposes a reduced-order small-signal closed-loop transfer function model based on Jordan continued-fraction expansion to assess the dynamic characteristics of the droop-controlled inverter and provide the preprocessing method for the real-time simulation of power systems. Firstly, dynamic phasors, time delay and zero-order hold are embedded into the small-signal model at the same time, then the closed-loop transfer function of the droop-controlled inverter is built. Compared with the existing closed-loop transfer function approaches, the accuracy of the built transfer function model is dramatically enhanced. Meanwhile, the inner cascaded voltage/current controller parameters are also designed. In order to directly obtain and preserve the maximum overshoot and settling time, which are main features to evaluate the system input-output dynamic response characteristics, the reduced second order closed-loop transfer function is proposed through the continued-fraction expansion regarding arbitrary points on the real frequency axis. Therein, this second order closed-loop transfer function with dynamic response of the original inverter is reduced to the lowest order. Furthermore, combined with the impedance-based approach, the proposed stability assessment approach is utilized to analyze the stability of the microgrid with multiple converters. Finally, simulations and experimental results demonstrate the convenience and accuracy of the proposed approach.
89 citations
TL;DR: In this article, a method for inverse system design using machine learning was proposed and applied to Raman amplifier design, which can be applied to other inverse problems in optical communication and photonics in general.
Abstract: A wide range of highly–relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the goal is to determine the corresponding set of input parameters (usually a set of input voltages, currents, powers, and wavelengths). We present a novel method for inverse system design using machine learning and apply it to Raman amplifier design. Inverse system design for Raman amplifiers consists of selecting pump powers and wavelengths that would result in a targeted gain profile. This is a challenging task due to highly–complex interaction between pumps and Raman gain. Using the proposed framework, highly–accurate predictions of the pumping setup for arbitrary Raman gain profiles are demonstrated numerically in C and C+L–band, as well as experimentally in C band, for the first time. A low mean (0.46 and 0.35 dB) and standard deviation (0.20 and 0.17 dB) of the maximum error are obtained for numerical (C+L–band) and experimental (C–band) results, respectively, when employing 4 pumps and 100 km span length. The presented framework is general and can be applied to other inverse problems in optical communication and photonics in general.
55 citations
TL;DR: Experimental results confirm the efficiency of the proposed approach in improving the classification accuracy compared to other meta-heuristic algorithms, and the superiority of X-shaped transfer function over commonly used S-shaped and V- shaped transfer functions.
Abstract: Definitive optimization algorithms are not able to solve high dimensional optimization problems when the search space grows exponentially with the problem size, and an exhaustive search also becomes impractical. To encounter this problem, researchers use approximation algorithms. A category of approximation algorithms is meta-heuristic algorithms which have shown an acceptable degree of efficiency to solve this kind of problems. Social Mimic Optimization (SMO) algorithm is a recently proposed meta-heuristic algorithm which is used to optimize problems with continuous solution space. It is proposed by following the behavior of people in society. SMO can efficiently explore the solution space for obtaining optimal or near-optimal solution by minimizing a given fitness function. Feature selection is a binary optimization problem where the aim is to maximize the classification accuracy of a learning algorithm using minimum the number of features. To convert the continuous search space to a binary one, a proper transfer function is required. The effect a transfer function has on the binary variant of an optimization algorithm is very important since selecting a particular subset of features based on the solution values attained by the algorithm in continuous search space depends on the considered transfer function. To this end, we have proposed a new transfer function, namely X-shaped transfer function, to enhance the exploration and exploitation ability of binary SMO. The proposed X-shaped transfer function utilizes two components and crossover operation to obtain a new solution. Effect of the proposed X-shaped transfer function is compared with the effect of four S-shaped and four V-shaped transfer functions on SMO in terms of achieved classification accuracy, rate of convergence, and number of features selected over 18 standard UCI datasets. The proposed algorithm is also compared with state-of-the-art meta-heuristic feature selection (FS) algorithms. Experimental results confirm the efficiency of the proposed approach in improving the classification accuracy compared to other meta-heuristic algorithms, and the superiority of X-shaped transfer function over commonly used S-shaped and V-shaped transfer functions. The source code of the proposed method along with the datasets used can be found at https://github.com/Rangerix/SocialMimic .
47 citations
TL;DR: The concentration of this paper lies in deriving the design conditions for the desired resilient filter such that the filtering error system preserves asymptotic stability as well as the $\boldsymbol {\mathcal {L}}_{\boldsy symbol {\infty }}$ -norm of the transfer function from the external disturbance input to the filteringerror output is below a specified bound.
Abstract: This paper studies the estimation problem for a class of nonlinear tunnel diode circuits with parameter perturbation. The dynamics of the nonlinear circuit is approximated by the Takagi–Sugeno fuzzy model with linear fractional parametric uncertainties. Considering the fuzzy filter with gain uncertainties, a filtering error system can be gotten naturally. The concentration of this paper lies in deriving the design conditions for the desired resilient filter such that the filtering error system preserves asymptotic stability as well as the $\boldsymbol {\mathcal {L}}_{\boldsymbol {\infty }}$ -norm of the transfer function from the external disturbance input to the filtering error output is below a specified bound. The filter design conditions for guaranteeing the prescribed peak-to-peak performance of the filtering error system are provided by a set of linear matrix inequalities. Finally, a simulation result is presented to illustrate the validity and effectiveness of the proposed filtering design for the parameter-controlled tunnel diode circuit.
42 citations
TL;DR: In this article, an improved phase compensation (IPC) method is proposed, which does not cause peaks in the magnitude response of sensitivity transfer function while providing sufficient phase margin, and the effectiveness of the proposed method is proved in terms of the steadystate and transient performances, as well as the performance under the grid frequency deviation.
Abstract: Phase compensated proportional resonant (PR) controllers with phase leading angles embedded into resonant controllers, are used to improve the system stability, when the grid-connected inverter is connected to a weak grid with large grid impedance. However, an inappropriate phase leading angle will cause obvious anti-peak in the magnitude response of phase compensated PR controller. Thus, the phase leading angle should be selected carefully. The conventional phase compensation (CPC) method for selecting the phase leading angle can maximize the system phase margin, but at the expense of large peaks in the magnitude response of sensitivity transfer function. Meanwhile, the bandwidths of resonant controllers will be reduced. In the paper, an improved phase compensation (IPC) method is proposed, which does not cause peaks in the magnitude response of sensitivity transfer function while providing sufficient phase margin. Experimental tests were carried out on a single phase grid-connected inverter. The effectiveness of the proposed method is proved in terms of the steady-state and transient performances, as well as the performance under the grid frequency deviation.
38 citations
TL;DR: A modified complex analysis of FFSOGI-QSG using fractional-order-based conformal mapping approach has been presented, and an adaptive AFFSOGI -QSG is proposed, which can adapt itself to the grid frequency variations through the adjustment of single tuning parameter.
Abstract: The frequency-fixed second-order generalized integrator-based quadrature signal generator (FFSOGI-QSG) has been widely used in grid synchronization applications. The major limitations of using this technique are the unequal amplitudes of in-phase and quadrature-phase signals under grid frequency variation, which causes double-frequency oscillatory and offset errors in FFSOGI-based phase-locked loop output. To overcome the issues, in this letter, a modified complex analysis of FFSOGI-QSG using fractional-order-based conformal mapping approach has been presented, and an adaptive FFSOGI-QSG (AFFSOGI-QSG) is proposed. The proposed AFFSOGI-QSG can adapt itself to the grid frequency variations through the adjustment of single tuning parameter, i.e., fractional-order gain. The proposed structure is also found to inherit the simplicity of the FFSOGI-QSG without changing the order of the system while demonstrating an improved disturbance rejection capability under various grid disturbances. Performance of the proposed AFFSOGI-QSG is finally validated against some well-known SOGI-QSGs using numerical results obtained from the MATLAB and experimental results from the dSPACE DS1104 hardware.
33 citations
01 Jan 2020
TL;DR: This paper proposes a novel transfer function with tunable parameters that allows different U-shaped transfer functions that can significantly improve the performance of binary particle swarm optimisation.
Abstract: Particle swarm optimisation (PSO), one of the most elegant algorithms in the field of nature-inspired optimisation, has many variants for solving different types of problems. One of these variants is binary particle swarm optimisation (BPSO), which is suitable for solving combinatorial optimisation problems. A main component of BPSO is the transfer function that maps continuous velocity values to probability values which in turn are used to update particle positions. Transfer function has a significant impact on the performance of BPSO algorithm. This paper proposes a novel transfer function with tunable parameters that allows different U-shaped transfer functions. For evaluating the proposed transfer functions, a set of benchmark functions and 0/1 knapsack problems are employed. The results show that the U-shaped transfer functions can significantly improve the performance of BPSO. It is also shown that the BPSO algorithms equipped with U-shaped transfer functions provide superior results compared to the existing transfer functions in the literature.
27 citations
TL;DR: A unified analysis where five popular fast diffraction calculation methods are analyzed from the perspective of phase space optics and the sampling theorem: single fast Fourier transform-based Fresnel transform, Fresnel transfer function approach, Fresnels impulse response approach, angular spectrum method, and Rayleigh-Sommerfeld convolution.
Abstract: Diffraction calculations are widely used in applications that require numerical simulation of optical wave propagation. Different numerical diffraction calculation methods have their own transform and sampling properties. In this study, we provide a unified analysis where five popular fast diffraction calculation methods are analyzed from the perspective of phase space optics and the sampling theorem: single fast Fourier transform-based Fresnel transform, Fresnel transfer function approach, Fresnel impulse response approach, angular spectrum method, and Rayleigh-Sommerfeld convolution. The evolutions of an input signal's space-bandwidth product (SBP) during wave propagation are illustrated with the help of a phase space diagram (PSD) and an ABCD matrix. It is demonstrated that all of the above methods cannot make full use of the SBP of the input signal after diffraction; and some transform properties have been ignored. Each method has its own restrictions and applicable range. The reason why different methods have different applicable ranges is explained with physical models. After comprehensively studying and comparing the effect on the SBP and sampling properties of these methods, suggestions are given for choosing the proper method for different applications and overcoming the restrictions of corresponding methods. The PSD and ABCD matrix are used to illustrate the properties of these methods intuitively. Numerical results are presented to verify the analysis, and potential ways to develop new diffraction calculation methods are also discussed.
26 citations
TL;DR: The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.
Abstract: Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.
26 citations
TL;DR: It is indicated that approximate models can considerably influence practical performance of optimally tuned FOPID control systems and ignorance of limitations of approximation methods in optimal tuning solutions can significantly affect real world performances.
TL;DR: The MIMO generalized Bode criterion is proposed, a stability criterion based on the Nyquist generalized stability criterion that can be applied to any system and is simple to use, as it only requires information contained in the open-loop transfer matrix and the Bode diagram.
Abstract: Three-phase dynamic systems and multiphase generators are frequently modeled and controlled in the synchronous reference frame. To properly model the cross-coupling terms in this reference frame, complex vector theory and transfer function matrices are commonly applied, obtaining multiple-input multiple-output (MIMO) dynamic models. The stability of MIMO systems can be assessed through the Nyquist generalized stability criterion. However, the use of the Nyquist diagram complicates the controller design. The Bode diagram is a more intuitive tool for the controller design; however, the Bode stability criterion is not applicable to MIMO systems. In this article, the MIMO generalized Bode criterion is proposed. Since this stability criterion is based on the Nyquist generalized stability criterion, it can be applied to any system. Furthermore, it is simple to use, as it only requires information contained in the open-loop transfer matrix and the Bode diagram. The proposed stability criterion thus offers an interesting tool for the controller design procedure in MIMO systems, as it is shown in this article for two common applications: the current control loop of a power converter, a $2\times2$ system, and the current control loop of two independent power converters in parallel, a $4\times4$ system.
TL;DR: In this paper, the authors consider a periodically time-varying base flow and perform a frequency-domain analysis of periodic perturbations about this base flow, which is governed by the harmonic resolvent, a linear operator similar to the harmonic transfer function introduced by Wereley.
Abstract: We propose a framework that elucidates the input–output characteristics of flows with complex dynamics arising from nonlinear interactions between different time scales. More specifically, we consider a periodically time-varying base flow, and perform a frequency-domain analysis of periodic perturbations about this base flow. The response of these perturbations is governed by the harmonic resolvent, which is a linear operator similar to the harmonic transfer function introduced by Wereley (1991 Analysis and control of linear periodically time-varying systems, PhD thesis, Massachusetts Institute of Technology). This approach makes it possible to explicitly capture the triadic interactions that are responsible for the energy transfer between different time scales in the flow. For instance, perturbations at frequency are coupled with perturbations at frequency through the base flow at frequency . We draw a connection with resolvent analysis, which is a special case of the harmonic resolvent when evaluated about a steady base flow. We show that the left and right singular vectors of the harmonic resolvent are the optimal response and forcing modes, which can be understood as full spatio-temporal signals that reveal space–time amplification characteristics of the flow. Finally, we illustrate the method on examples, including a three-dimensional system of ordinary differential equations and the flow over an airfoil at near-stall angle of attack.
TL;DR: A novel hybrid-based neuro-TF technique which systematically combines both pole-residue and rational formats of the transfer functions is presented which can obtain better accuracy in challenging applications of large geometrical variations and high order.
Abstract: Neuro-transfer function (neuro-TF) approaches have become more and more popular in parametric modeling for electromagnetic (EM) behavior of microwave components. Existing pole-residue-based neuro-TF approach has better capability of dealing with high-order problem than the rational-based neuro-TF approach, but has the discontinuity issue and the associated non-smoothness issue of the poles/residues when the geometrical variations become large while the rational-based neuro-TF approach does not have. This paper addresses this situation and presents a novel hybrid-based neuro-TF technique which systematically combines both pole-residue and rational formats of the transfer functions. Starting with the pole-residue-based transfer functions, we propose a novel technique to automatically identify the poles/residues that are smooth-continuous and the poles/residues that have the discontinuity and non-smoothness issues. The proposed technique converts the poles/residues that have those issues into the coefficients of the rational-based transfer function to solve the discontinuity and non-smoothness issues in the existing pole-residue-based neuro-TF approach. The proposed technique remains the smooth-continuous poles/residues in the pole-residue format of the transfer function to maintain the capability of handling high-order problem. Compared with the existing neuro-TF modeling methods, the proposed technique can obtain better accuracy in challenging applications of large geometrical variations and high order. The proposed technique is illustrated by two examples of parametric modeling of microwave components.
TL;DR: The obtained results of this paper show the powerful ability of local fractional calculus in the analysis of complex problems arising in engineering fields.
Abstract: The local fractional derivative (LFD) has attracted wide attention in the field of engineering application. In this paper, the LFD is used to model the fractional Sallen-Key filter for the first time. The non-differentiable(ND) transfer function is obtained by using the local fractional Laplace transform(LFLT). And the amplitude frequency response is analyzed in detail for different fractional order ς. It is found that the fractional Sallen-Key filter becomes the ordinary one in the special case ς = 1. The obtained results of this paper show the powerful ability of local fractional calculus in the analysis of complex problems arising in engineering fields.
TL;DR: Using a theorem proved, it is shown that the stability of fuzzy linear dynamical systems can also be investigated by a matrix called the granular fuzzy Routh–Hurwitz matrix.
Abstract: This paper deals with the investigation of the stability of fuzzy linear dynamical systems using the new notion called granular fuzzy Laplace transform. In order to analyzing the stability, some new notions have been introduced such as granular improper fuzzy integral, granular fuzzy Laplace transform, equilibrium points, granular fuzzy transfer function, and etc.. Based on the concept of granular metric, the fuzzy marginal and asymptotic stability of fuzzy dynamical systems are defined. Moreover, using the granular fuzzy Laplace transform, the concept of fuzzy poles and fuzzy zeros are presented. The findings shed light on the advantages and efficiency of the granular fuzzy Laplace transform in comparison with the previous definition of fuzzy Laplace transform. Furthermore, using a theorem proved in this paper we show that the stability of fuzzy linear dynamical systems can also been investigated by a matrix called the granular fuzzy Routh–Hurwitz matrix.
TL;DR: The two-port network theory is employed to integrate the MIMO impedance models into a single-input single-output (SISO) open-loop gain, which is composed by a ratio of two SISO impedances.
Abstract: Converter–grid interactions tend to bring in frequency-coupled oscillations that deteriorate the grid stability and power quality. The frequency-coupled oscillations are generally characterized by means of multiple-input multiple-output (MIMO) impedance models, which requires using the multivariable control theory to analyze resonances. In this article, instead of the MIMO modeling and analysis, the two-port network theory is employed to integrate the MIMO impedance models into a single-input single-output (SISO) open-loop gain, which is composed by a ratio of two SISO impedances. Thus, the system resonance frequency can be readily identified with Bode plots and the classical Nyquist stability criterion. Case studies in both simulations and experimental tests corroborate the theoretical stability analysis.
TL;DR: A general fast linear control can be applied in the front-side converter to achieve optimum efficiency, while the load-side Converter regulates the output power independently and the parallel–parallel IPT converter gives the best efficiency.
Abstract: Inductive power transfer (IPT) systems with front-side and load-side converters are generally developed for better overall system efficiency than a single IPT converter by providing maximum efficiency tracking and output regulation against variations of coupling coefficient ( $k$ ) and load. The optimum efficiency point of an IPT converter is at a particular loading resistance, which varies with $k$ and is hard to measure directly for the purpose of control. Perturb and observe control is normally implemented to iteratively track the optimum efficiency point. However, this heuristic algorithm results in slow response to the variations of $k$ and load. Recently, a much faster linear control for optimum efficiency tracking has been developed for the series-series (SS) IPT system only. This paper proposes a general linear control scheme for all four basic IPT systems to track the optimum efficiency point. Unlike the SS IPT converter, it is found that additional control of either frequency or adaptive compensation is needed for the other three basic IPT converters to operate against the variation of $k$ . Different input to output transfer functions and their $k$ - and load-independent properties at optimum efficiency are identified for all four basic IPT systems. Thus, a general fast linear control can be applied in the front-side converter to achieve optimum efficiency, while the load-side converter regulates the output power independently. Furthermore, the maximum efficiencies of the IPT converters with these four basic compensations for an identical loosely-coupled transformer are compared theoretically and verified experimentally. The parallel–parallel IPT converter gives the best efficiency. Its additional frequency control in conjunction with the optimum efficiency tracking and the output power regulation is also experimentally validated.
TL;DR: An improved PPC regulator is proposed, which can rapidly and accurately track ac current references and realize decoupling control among phase currents, and features fast-response-performance and high-ac-tracking-accuracy.
Abstract: The traditional pulsewidth modulation predictive control (PPC) has poor steady-state performance when used for fault-tolerant control of multi-phase machine drives, because this paper finds that PPC theoretically has a large steady-state error when tracking ac signals. Thus, an improved PPC regulator is proposed, which can rapidly and accurately track ac current references and realize decoupling control among phase currents. The proposed method is developed by redesigning the transfer function of the conventional PPC to a resonant form, reducing the amplitude and phase error in the conventional approach. By making the resonant frequency of the resonant term track the machine frequency, the amplitude and phase response of the system at the working frequency are close to zero. Thus, the proposed R-PPC features fast-response-performance and high-ac-tracking-accuracy. The effectiveness of the proposed method has been verified on a surface-mounted permanent magnet synchronous machine by experiments.
TL;DR: The function space optimization (FSO), a symbolic regression method for estimating parameter transfer functions for distributed hydrological models, is presented and it is shown that FSO is able to estimate transfer functions correctly or approximate them sufficiently.
Abstract: Estimating parameters for distributed hydrological models is a challenging and long studied task. Parameter transfer functions, which define model parameters as functions of geophysical properties of a catchment, might improve the calibration procedure, increase process realism, and can enable prediction in ungauged areas. We present the function space optimization (FSO), a symbolic regression method for estimating parameter transfer functions for distributed hydrological models. FSO is based on the idea of transferring the search for mathematical expressions into a continuous vector space that can be used for optimization. This is accomplished by using a text generating neural network with a variational autoencoder architecture that can learn to compress the information of mathematical functions. To evaluate the performance of FSO, we conducted a case study using a parsimonious hydrological model and synthetic discharge data. The case study consisted of two FSO applications: single-criteria FSO, where only discharge was used for optimization, and multicriteria FSO, where additional spatiotemporal observations of model states were used for transfer function estimation. The results show that FSO is able to estimate transfer functions correctly or approximate them sufficiently. We observed a reduced fit of the parameter density functions resulting from the inferred transfer functions for less sensitive model parameters. For those it was sufficient to estimate functions resulting in parameter distributions with approximately the same mean parameter values as the real transfer functions. The results of the multicriteria FSO showed that using multiple spatiotemporal observations for optimization increased the quality of estimation considerably.
10 Aug 2020
TL;DR: In this paper, the unilateral and bilateral Laplace transforms are compared in the one-dimensional case, leading to the formulation of the initial-condition theorem, and the case of fractional-order systems is also included.
Abstract: The paper reviews the unilateral and bilateral, one- and two-dimensional Laplace transforms. The unilateral and bilateral Laplace transforms are compared in the one-dimensional case, leading to the formulation of the initial-condition theorem. This problem is solved with all generality in the one- and two-dimensional cases with the bilateral Laplace transform. The case of fractional-order systems is also included. General two-dimensional linear systems are introduced and the corresponding transfer function is defined.
TL;DR: A new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix ( HWOM ) to reduce the mathematical complexity resulting from the fractional derivatives of signals.
Abstract: The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix ( HWOM ) . To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.
11 Oct 2020
TL;DR: In this article, the authors derived a small signal model of bidirectional dc-dc dual active bridge (DAB) converter under multi-phase shift including the conventional single phase shift (SPS), dual phase shift(DPS), and triple phase shift modulation.
Abstract: The derivation of small signal model of bidirectional dc-dc dual active bridge (DAB) converter under multi-phase shift including the conventional single phase shift (SPS), dual phase shift (DPS) and triple phase shift (TPS) modulation is presented in this paper. Full order continuous time generalized average model has been developed for different modulation strategies and control variables to output voltage transfer functions analytical equations have been derived. Also, open loop responses of the DAB converter based on the developed model have been plotted and frequency response estimation simulation is performed in Simulink to compare the accuracy of the model. Finally, a proportional integral (PI) controller has been designed for each of the modulation methods and the effectiveness of the controller has been demonstrated through simulation.
TL;DR: A new theory for continuous time PID controller design is proposed using a dominant pole placement method mapped on to the discrete time domain with an appropriate choice of the sampling time to convert the delays in to finite number of poles.
Abstract: Time delay handling is a major challenge in dominant pole placement design due to variable number of poles and zeros arising from the approximation of the delay term. This paper proposes a new theory for continuous time PID controller design using a dominant pole placement method mapped on to the discrete time domain with an appropriate choice of the sampling time to convert the delays in to finite number of poles. The method is developed to handle linear systems, represented by second-order plus time delay (SOPTD) transfer function models. The proposed method does not contain finite-term approximations such as various orders of Pade, for handling the time delays, which may affect the number and orientation of the resulting poles/zeros. Effectiveness of the proposed method have been shown using numerical simulations on nine SOPTD test-bench processes and another six challenging processes including single, double integrators, and process with zero damping.
TL;DR: A new method of data-driven modeling for a class of multiple-transmitter single-receiver wireless power transfer (WPT) systems that yields parsimonious models, whose parameters are directly estimated from input–output data is developed.
Abstract: This article develops a new method of data-driven modeling for a class of multiple-transmitter single-receiver wireless power transfer (WPT) systems. A continuous-time multiple-input single-output (MISO) model with pure time delays is used to characterize the input–output behavior of the system, where the transfer functions associated with each input channel are not constrained to have the same denominator. Moreover, the time delays are allowed to be a fraction of the sampling interval in order to account for the delay effects that stem from circuit components and wireless communication, which are, by nature, often a fraction of the sampling interval. An optimal refined instrumental variable method is proposed to estimate the parameters and time delays of the MISO model based on sampled input–output data. In contrast to the conventional circuit-theory-based modeling methods that rely on circuit parameters and result in models which are often complex, the proposed data-based method yields parsimonious models, whose parameters are directly estimated from input–output data. Due to the easy availability of sampled data in control engineering applications, the proposed method is clearly more user-friendly, having a broad prospect for efficient operation of WPT systems, such as prediction, optimization, and control. Numerical and experimental results are presented to validate the effectiveness and merit of the proposed method.
TL;DR: A comparative analysis of TL theory and multipath signal propagation models with the proposed Simulink model is presented to validate the performance and accuracy of proposed model, which will pave the way to improve the efficiency of next-generation NB-PLC technologies.
Abstract: This paper is focused on the channel modeling techniques for implementation of narrowband power line communication (NB-PLC) over medium voltage (MV) network for the purpose of advanced metering infrastructure (AMI). Three different types of models, based on deterministic method, statistical method, and network parameters based method are investigated in detail. Transmission line (TL) theory model is used to express the MV network as a two-port network to examine characteristics of sending and receiving NB-PLC signals. Multipath signal propagation model is used to incorporate the effect of multipath signals to determine the NB-PLC transfer function. A Simulink model is proposed which considers the values of MV network to examine the characteristics of NB-PLC signals. Frequency selectivity is also introduced in the impedances to compare variations and characteristics with constant impedances based MV network. A state-of-the-art mechanism for the modeling of capacitive coupling device, and impedances of low voltage (LV) and MV networks is developed. Moreover, a comparative analysis of TL theory and multipath signal propagation models with the proposed Simulink model is presented to validate the performance and accuracy of proposed model. This research work will pave the way to improve the efficiency of next-generation NB-PLC technologies.
TL;DR: A comparison of two promising frequency-lifted representations used in the state-space modeling of power-electronic converters: dynamic phasors and harmonic state- space is conducted.
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TL;DR: The AAA framework for approximating multivariate functions where the approximant is constructed in the multivariate Barycentric form is developed and an extension to the case of matrix-valued functions is discussed, i.e., multi-input/multi-output dynamical systems, and a connection to the tangential interpolation theory is provided.
Abstract: The AAA algorithm has become a popular tool for data-driven rational approximation of single variable functions, such as transfer functions of a linear dynamical system. In the setting of parametric dynamical systems appearing in many prominent applications, the underlying (transfer) function to be modeled is a multivariate function. With this in mind, we develop the AAA framework for approximating multivariate functions where the approximant is constructed in the multivariate Barycentric form. The method is data-driven, in the sense that it does not require access to full state-space data and requires only function evaluations. We discuss an extension to the case of matrix-valued functions, i.e., multi-input/multi-output dynamical systems, and provide a connection to the tangential interpolation theory. Several numerical examples illustrate the effectiveness of the proposed approach.
TL;DR: In this paper, a Laplace-domain method is proposed to predict time-domain motion responses of floating structures, and two techniques of generalising transfer functions of floating stru... are presented.
Abstract: A Laplace-domain method is proposed to predict time-domain motion responses of floating structures. As a theoretical contribution, two techniques of generalising transfer functions of floating stru...
TL;DR: It is shown that the measurement time of a 3D system matrix is reduced by 96%.
Abstract: Image reconstruction in magnetic particle imaging is often performed using a system matrix based approach. The acquisition of a system matrix is a time-consuming calibration which may take several weeks and thus, is not feasible for a clinical device. Due to hardware characteristics of the receive chain, a system matrix may not even be used in similar devices but has to be acquired for each imager. In this work, a dedicated device is used for measuring a hybrid system matrix. It is shown that the measurement time of a 3D system matrix is reduced by 96%. The transfer function of the receive chains is measured, which allows the use of the same system matrix in multiple devices. Equivalent image reconstruction results are reached using the hybrid system matrix. Furthermore, the inhomogeneous sensitivity profile of receive coils is successfully applied to a hybrid system matrix. It is shown that each aspect of signal acquisition in magnetic particle imaging can be taken into account using hybrid system matrices. It is favourable to use a hybrid system matrix for image reconstruction in terms of measurement time, signal-to-noise ratio and discretisation.