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The unmanned aerial vehicle (UAV) is inexpensive and offers a fast response speed and robust flexibility; thus, it is a promising tool in the maritime buoyage inspection scenario, which involves monitoring and accessing a lateral mark system far away from the coast. However, two main problems can occur during inspection. The first is extreme weather conditions, resulting in a deviation between the inspection route and the design route. The second is that the buoyage beacons are visited only once. Therefore, this article proposes a buoyage inspection system consisting of a single UAV and random coastal buoys. The UAV automatically takes off from the depot, performs a self-check on the buoy beacons, and then returns to the depot. A cascade active disturbance rejection controller (ADRC) is designed to adjust the real-time trajectory of the UAV system. A feasible trajectory planning method is also designed based on the continuous Hopfield neural network (CHNN) and genetic algorithm (GA) to minimize the inspection distance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed method.

Due to the rapid development of the Intelligent Traffic System (for short, ITS), the shared information among successive vehicles can be acquired accurately and reliably. In this paper, a control scheme utilizing the successive flux difference under honk environment (SFDHE) is investigated in the lattice hydrodynamic model. Based on the Hurwitz criteria and the $H_{\infty }$ -norm, the sufficient condition for the transfer function is derived with the control theory analysis. The Bode-plot of transfer function indicates that the stability of the traffic flow is increased significantly with the novel controller. Moreover, the numerical simulation results of the new model are compared with that of Peng’s model, which demonstrates that traffic jams can be suppressed efficiently when the SFDHE control signal is taken into account.

In recent years, virtual synchronous generator (VSG) control algorithm has been widely studied in distributed grid-connected power inverters to deal with active and reactive power adjustment by simulating the operation principle of the actual synchronous generator. Nevertheless, once the current sensor fails, the grid-connected power inverter will lose its stability. To enhance the fault-tolerant operation ability of the grid-connected power inverter, a new inverter side current sensor-less model predictive control method based on sliding mode observer (SMO) is proposed. The stability of the SMO is analyzed by using the Lyapunov stability criterion. The gains of the SMO are then designed by analyzing their influences on the current and voltage observation based on the closed-loop transfer functions. Besides, to show the influences of the capacitance changes on the current observation, a detailed parameter sensitivity analysis is also carried out. Then, by using the estimated inductance current from the SMO, a current sensor-less model predictive control method is finally proposed. The main novelty of the proposed method is that both the output voltage of the inverter and the inverter side inductance are not required in the proposed SMO. So, the accuracy of current sensor-less control is improved. To verify the effectiveness of the proposed method, detailed experimental studies are carried out based on a Typhoon and PE-Expert 4 based experimental platform.

Networked control systems can help build cost-effective and flexible industrial systems. A system that can function while being immune to cyberattacks is necessary. A method called fixed redundant path selection (FRPS) has been proposed to detect and mitigate data tampering attacks in a networked motion control system. This system contains redundant forward network paths from the controller to the motor sides to detect the attacked path by comparing the values that are received through respective paths. Then, a path selector on the motor side chooses a value on the path that is not attacked based on the majority decision. Increasing the number of redundant paths improves the detection performance of simultaneous attacks against multiple paths. However, it also increases the amount of traffic because the same data are transmitted to all of the redundant paths. This study proposes a dynamic redundant path selection (DRPS) method to balance the detection performance and the amount of traffic. The proposed method initially applies three redundant paths and changes the number of redundant paths to five only when the path selector detects a difference among the received values for the three paths. The experiments confirm that the proposed DRPS outperforms the conventional FRPS. The former can detect and mitigate the data tampering attacks while reducing the number of network paths during tampering detection when the system is exposed to simultaneous attacks against up to two of the redundant paths.

Nowadays, some of the most important challenges to society are focused on the use of renewable energies, mitigation of greenhouse-type gases and residual treatment. Microalgae are becoming one of the most powerful solutions to contribute to these problems. So, industrial facilities have been developed in the last decades to produce this type of microorganisms. However the dynamics of these systems is very complex, and advanced models and control strategies are required. The Interpolating Control is a novel technique with capacity to deal with state and control constraints with low computational burden, being suitable to be applied in this type of processes. This technique has been used in the literature to control simulated or laboratory scale systems. This paper shows the operation results for a Interpolated Controller in an industrial raceway photobioreactor to contribute in the control of these real processes.

Based on the common existence of the mixed road, a switched controller with consideration of the difference between estimation optimal and current flux (EOCFD) is presented in the lattice hydrodynamic model of traffic flow. Based on the Hurwitz criteria and the $H_{\infty }$ -norm, stability conditions for the curved road and straight road scenarios are obtained with the transfer functions $G_{0}$ and $G_{1}$ respectively. By analyzing the Bode-plot of transfer functions $G_{0}$ and $G_{1}$ , stability analysis is performed with the feedback gain $k$ , the radian $\theta _{j}$ and the curvature radius $R$ . These theoretical results indicate that the switched control scheme prompts the traffic flow to be more stable in both curved and straight roads. Compared with Cheng’s model, numerical simulations with multiple perturbations confirm that the switched control scheme can further suppress the traffic congestion with a lower feedback gain on mixed road.

The paper presents the designed system to measurements of gas sensor transient characteristics. Electrochemical oxygen sensor SK-25F was selected for measurements from point of view application in health care and ensuring human safety in industrial production facilities and laboratories. The aim of this work is to design an automatic system for measuring the transient function G(s) of electrochemical sensors and to identify the mathematic model from a sample of sensors. The automatic measurement system based on a microcontroller for the measurements of dynamical characteristics of gas sensors was designed and developed. The result is transfer function with fit to estimation data higher as 99.48 %. The bandwidth of gas sensors was calculated on average at 0.0285763 Hz with a maximum deviation of 0.34 %. Finally, an algorithm for detecting the exceeding of the frequency range of an electrochemical gas sensor is proposed. Its application is in speed control UAV with the aim of accurate measurements of gases in the air by electrochemical sensors.

Low-pass and high-pass non-integer order shelving filter designs, which are suitable for acoustic applications, are presented in this work. A first design is based on a standard fractional-order bilinear transfer function, while a second one is based on the transposition of the integer-order transfer function into its power-law counterpart. Both transfer functions are approximated using the Oustaloup approximation tool, while the implementation in the case of the power-law filters is performed through the employment of the concept of driving-point impedance synthesis. An attractive benefit is the extra degree of freedom, resulting from the variable order of both fractional-order and power-law filters, which allows improved design flexibility compared to the case of integer-order filters. From the implementation point of view, only one building block is required to realize both filter types, thanks to the employment of the Voltage Conveyor. European Union (European Social Fund~ESF) through the Operational Program Human Resources Development, Education and Lifelong Learning [MIS-5000432]; Istanbul Technical University VLSI Laboratories for the Cadence Design Environment This work was supported in part by the Greece and the European Union (European Social Fund~ESF) through the Operational Program Human Resources Development, Education and Lifelong Learning in the context of the project Strengthening Human Resources Research Potential via Doctorate Research-2nd Cycle implemented by the State Scholarships Foundation (IKY) under Grant MIS-5000432, and in part by the Istanbul Technical University VLSI Laboratories for the Cadence Design Environment.

This paper presents a reduced-order electrochemical battery model designed for the online implementation of battery control systems. The model is based on porous-electrode and concentrated-solution theory frameworks and is able to predict voltage as well as the internal electrochemical variables of a battery. The reduction of the model leads to a physics-based one-dimensional discrete-time state-space reduced-order model (ROM), which is especially beneficial for online systems. Models optimized around different operational setpoints are combined to predict cell variables over a wide range of temperatures and state of charges (SOCs) using the output-blending method. A sigma-point Kalman filter is further used to manage inaccuracies generated by the reduction process and experimental-related issues such as measurement error (noise) in the current and voltage sensors. The state-estimation accuracies are measured against a full-order model (FOM) developed in COMSOL. The whole system is able to track the internal variables of the cell, as well as the cell voltage and SOC with very high accuracy, demonstrating its suitability for an online battery control system.

It is worth noting that a graphical equalizer is a linear time-invariant system. Hence, its input-output relationship can be modeled by a transfer function. The graphical equalizer is realized by a parallel connection of a set of filters called a filter bank. Each filter in the filter bank is designed to equalize a certain frequency band of the audio signal. There are two types of graphic equalizers. The first type of graphical equalizers is based on a finite impulse response (FIR) filter bank. However, it requires very high order FIR filters to achieve a certain degree of equalization precision. As a result, the required computational power is very high. Also, the output of the graphical equalizer is suffered from a very long time delay. The second type of graphical equalizers is based on an infinite impulse response (IIR) filter bank. It can address the above issues. That is, its implementation cost is low. To design each IIR filter, the total absolute error between the ideal equalization and the actual equalization in the passband of the IIR filter is minimized subject to the constraints imposed on the stability criterion of the IIR filter as well as on the maximum modulus error between the ideal equalization and the actual equalization in the corresponding passband. This design problem is a functional inequality constrained optimization problem which consists of an infinite number of inequality constraints. By performing the downsampling in the frequency domain, the infinite constrained optimization problem becomes the finite constrained optimization problem. By using the genetic algorithm to find the solution of this optimization problem, the coefficients of all the IIR filters are obtained. The computer numerical simulation results show that the equalizer designed by our proposed method yields a good equalization performance.

This work was supported by Czech Science Foundation under Project 19-24585S. This paper introduces the generalization of the classical Transitional Butterworth-Butterworth Filter (TBBF) to the Fractional-Order (FO) domain. Stable rational approximants of the FO-TBBF are optimally realized. Several design examples demonstrate the robustness and modeling efficacy of the proposed method. Practical circuit implementation using the current feedback operational amplifier employed as an active element is presented. Experimental results endorse good agreement (R-2 = 0.999968) with the theoretical magnitude-frequency characteristic. Publisher's Version Q2 WOS:000698839200001

The high-power Asymmetric half-bridge Converter (AHBC) LED constant current driver controlled by digital current mode is a fourth-order system. Static operating point, parasitic resistance, load characteristics, sampling effect, modulation mode and loop delay will have great influence on its dynamic performance. In this paper, the small-signal pulse transfer function of the driver is established by the discrete-time modeling method for two operating points with three modulation modes of trailing-edge, leading-edge and double-edge. And, the effects of parasitic parameters, delay, sampling and load etc. are fully considered in modeling. For a large number of complex exponential matrix operations, the first order Taylor formula is used for approximate calculation after the coefficient matrix is obtained by substituting the data. Then, the designed average current mode compensators based on state-space averaging small-signal model(SSA) are used for analog and digital control, and the loop characteristics and control performance are analyzed. Simulation and experimental comparison results show that the proposed discrete-time model(DTM) can more accurately describe the characteristics of resonant peak, high-frequency dynamic and loop delay, and is very suitable for the design of high frequency digital controller.

This work presents a novel approach for the accurate estimation of multiple time-delays from the frequency response of a distributed system. The proposed approach is based on a powerful and flexible machine learning technique, namely, the least-square support vector machine (LS-SVM). The LS-SVM regression is used to construct a metamodel of the transfer function describing a generic linear time-invariant system in a delayed-rational form. Specifically, after some manipulation the LS-SVM model precisely identifies the dominant propagation delays of the original system. The essential steps and critical criteria for the delay identification procedure are carefully discussed throughout the paper. Once the system delays have been identified, the rational part of the metamodel expansion is then obtained by means of a progressive application of the conventional vector fitting algorithm. Numerical examples are presented to illustrate the feasibility and performance of the proposed technique and to compare its performances with what is provided by state-of-the-art techniques. The results clearly highlight the capability of the proposed approach to identify the dominant delays in distributed systems, thus allowing to construct compact delayed rational models.

In the manufacturing industry, the high acceleration demands lead to the lightweight designs of the stages, while it may cause undesirable vibrations. In order to obtain the most efficient vibration suppression performance, this paper proposes a co-optimization method to realize the simultaneous optimization of the Actuators and Sensors (AS) placement and the controller parameters. The co-optimization method includes an inner cycle for minimizing the control energy by a Linear Quadratic Gaussian (LQG) controller, and an outer cycle for optimizing the closed-loop 1-norm is constructed as the criteria for the outer optimization cycle. Besides, the Dynamic Opposite Learning Enhanced Teaching-Learning-Based Optimization (DOLTLBO) algorithm is applied to enhance the searching ability. Numerical experiments on a lightweight stage were performed to validate the co-optimization method. The results reveal that the new method has a strong ability in optimizing the AS placement and the controller parameters, and can finally guarantee superior performances for vibration suppression.

During the past few decades, two and higher dimensional systems have been extensively applied in many areas of research. The representation of the 2-D systems in the frequency domain is usually given by its transfer function. The bounded-input bounded-output (BIBO) stability of the two dimensional discrete systems depends on the zeros of the characteristic polynomial which is the denominator of this transfer function. In this paper, a new sufficient criterion for the stability of two-dimensional linear shift-invariant discrete systems is presented. The new criterion is based on the sufficient condition for stable polynomials with complex coefficients and the stability criterion for 2-D discrete systems proposed by Murray and Delsarte et al.. The new criterion is non-conservative for the stability testing of 2-D discrete systems. It is shown that the proposed sufficient criterion is simple enough to be applied for the stability checking of the 2-D discrete systems. The utility of the proposed criterion is demonstrated by examples.

With the expansion of the use of frequency response analysis (FRA) as a reliable tool for fault detection in transformers, more capabilities of this method are discovered every day. So that today the number of transformer faults that can be identified by FRA method has also increased. One of the most critical steps in fault detection with FRA is to distinguish faults and classify them in different classes. In this paper, well-known intelligent classifiers (probabilistic neural network, decision tree, support vector machine, and k-nearest neighbors) are used to classify transformer faults. For this purpose, the necessary measurements are performed on the model transformers under the healthy condition and under different fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on numerical and statistical indices for training and validation of classifiers is proposed. After completing the training process, the performance of the classifiers is evaluated and compared by applying the data obtained from real transformers.

Seagull Optimization Algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eight different binary versions of SOA are proposed in this paper. The proposed algorithm uses four transfer functions, S-shaped and V-shaped, which are used to map the continuous search space into discrete search space. Twenty-five benchmark functions are used to validate the performance of the proposed algorithm. The statistical significance of the proposed algorithm is also analysed. Experimental results divulge that the proposed algorithm outperforms the competitive algorithms. The proposed algorithm is also applied on data mining. The results demonstrate the superiority of binary seagull optimization algorithm in data mining application.

As a continuous antenna, the leaky coaxial cable (LCX) is widely used in wireless communication of closed or semi-closed areas such as the subway, mine, tunnel, etc. Usually, LCX networks are required in coal mines. Its channel characteristics are of great significance to mobile communication and mine safety. This paper presents an effective channel modeling and optimization method that combines the state transition and the particle swarm optimization (PSO) algorithms for the LCX network. First, a channel model can be established with a few parameters based on the state transition method, which can describe the complex transfer function of the LCX network; then, taking the transfer characteristics of the LCX network as the optimization objective, which should be consistent with the measured results. And the established objective function is optimized and designed with the PSO algorithm. Finally, the proposed method is validated by comparing the measured results and the model-based simulations on two test LCX networks. The experimental results show that the absolute error between the proposed model and the measurement model is less than that of other models at any frequency point. The proposed model can accurately obtain all responses between point-to-point channels at one time. It will help to accurately obtain the performance of different LCX networks and help to detect and locate faults in future studies.

In this paper a comprehensive procedure for the analog modeling of Fractional-Order Elements (FOEs) is presented. Unlike most already proposed techniques, a standard approach from classical circuit theory is applied. It includes the realization of a system function by a mathematical approximation of the desired phase response, and the synthesis procedure for the realization of basic fractional-order (FO) one-port models as passive RC Cauer- and Foster-form canonical circuits. Based on the presented one-ports, simple realizations of two-port differentiator and integrator models are derived. Beside the description of the design procedure, illustrative examples, circuit diagrams, simulation results and practical realizations are presented.

The paper presents the simple and efficient analysis method of frequency-domain to control the core power and coolant temperature of Nuclear Power Plants (NPPs). In order to properly describe the neutron kinetic and thermal transmission of NPPs, a one-dimensional dynamic model has been accomplished by using the linearizing in time domain and discretizing in spatial domain, and then the transfer function matrix can be obtained through Laplace transform. Based on the transfer function matrix of NPPs, the effective of different system inputs: reactivity and feed water mass flow, can be analyzed by Nyquist method. On this basis, the power and temperature control system are presented, and the stability is proved in frequency domain. The proposed method is verified by RELAP5 that confirm its performance.

This paper proposes a detection technique for the chipless RFID system. In the proposed technique, the experiments for measuring the scattering responses of all possible tags were conducted first in a free-space anechoic chamber in order to avoid the effect of unwanted signals due to the environment. The responses of all possible tags were exploited in order to extract poles, including natural frequencies and damping factors, by using the short-time matrix pencil method. All extracted poles successively chosen from the late-time portions were exploited to create the decision boundary by using the k- nearest neighbor algorithm. In order to validate the robustness of the proposed detection technique, experiments with the chipless RFID system with tags attached to containers, i.e. parcel and plastic boxes, were conducted. Poles extracted from the response of the tag attached to a container were fed to the decision boundary for ID detection. The poles that had fallen into the region labeled as logic “1” were detected as logic “1,” and vice versa . The experiment results showed that the attachment of the tags to the containers caused a change in the poles. The conventional technique using only natural frequencies has exhibited poor performance regarding tag ID detection. On the other hand, the proposed detection technique achieved a 100% detection rate, although the extracted poles used to detect the bit logic were disturbed by the container. The experimental results confirm the superiority of the proposed system over conventional chipless RFID detection. Moreover, the proposed technique can be considered a robust detection technique.

This paper describes the design of analog pseudo-differential fractional frequency filter with the order of $(2+\alpha)$ , where $0 . The filter operates in a mixed-transadmittance mode (voltage input, current output) and provides a low-pass frequency response according to Butterworth approximation. General formulas to determine the required transfer function coefficients for desired value of fractional order $\alpha $ are also introduced. The designed filter provides the beneficial features of fully-differential solutions but with a less complex circuit topology. It is canonical, i.e. it employs a minimum number of passive elements, whereas all are grounded, and current conveyors as active elements. The proposed structure offers high input impedance, high output impedance, and high common-mode rejection ratio. By simple modification, voltage response can also be obtained. The performance of the proposed frequency filter is verified both by simulations and experimental measurements proving the validity of theory and the advantageous features of the filter.

The introduction of electrolytic aluminum load in a large-scale power system with complex power station structure is essential for load frequency control (LFC). In this paper, we propose a control strategy for electrolytic aluminum load to participate in the frequency modulation of the power grid based on the IEEE standard two-region LFC model. Besides, a fractional-order PID (FOPID) controller is designed and compared with traditional PID, and the parameters of the FOPID controller are optimized by PSO algorithm. In addition, a two-region LFC model was built to verify the integrated control strategy for electrolytic aluminum load participation in frequency modulation, which show that the FOPID controller optimized by the PSO algorithm has stronger robustness in the LFC.

The second-order generalized integrator based orthogonal signal generator (SOGI-OSG) is commonly used to produce direct and quadrature axis ( $dq$ -axes) signals in stationary reference frame for single-phase systems. Fast and robust OSG with harmonic rejection capability is required for parallel operation of power generation systems. The frequency response function (FRF) of classical OSG does not show robustness towards DC offset and grid harmonic disturbances compared to the direct signal of SOGI. This paper presents OSGs based on $n^{th}$ order bandpass filter with a phase shifter using a scaled $Q$ factor as a design variable. The frequency and time-domain performance analysis will be performed based on the scaled $Q$ factor to simplify the design process of the proposed OSGs. The −3dB frequency and settling step time will be matched in higher order OSGs selecting a scaled $Q$ factor. The proposed method will show enhanced harmonic rejection ratio of an additional −20dB with increasing OSG order with tradeoff in computational burden.

Accurate extraction and visualization of the target features are important in medical visualization, allowing the user to get a meaningful view of their targets. The traditional way to generate visualization results is 1D TF-based volume visualization, which uses a 1D -TF to determine the optical properties of each voxel. However, it is difficult to distinguish multiple targets by using the 1D TF-based volume visualization. It is also challenging to distinguish accurate targets when the objects’ intensity values are similar in volume. Using the traditional transfer function usually fails to extract important target features and generates less accurate results. In this paper, we proposed extracting and segmentation techniques based on K-means algorithm that allows the users to segment and enhances single or multiple targets by a single point to view the features in 3D view. We have applied it to various univariate or multivariate volume datasets from the medical field to demonstrate its effectiveness. Moreover, we have performed both qualitative and quantitative experiments to compare its results against the results from two state-of-the-art techniques and the ground truths. The experimental results showed that our method is able to generate the closest results to the ground truth.

Accurate and fast fault detection in transmission lines is of high importance to maintain the reliability of power systems. Most of the existing methods suffer from false detection of high-impedance faults. In this paper, the transfer function (TF) method is introduced to evaluate the effect of impedance and location of faults by analyzing the voltage and current signals in the frequency domain. Interpretation of the results of the TF method is considered as a weakness of this method. In order to alleviate this problem, a convolutional neural network (CNN) and the hybrid model of deep reinforcement learning (DRL) are utilized to identify and locate single-phase to ground short circuit faults in transmission lines. Single-phase to ground short circuit faults with various fault impedances are applied on an IEEE standard transmission line system. Then, the TF traces are calculated and are collected as input datasets for the proposed models. The fault location results for each network are evaluated via various statistical performance metrics such as correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE). The R-value of the CNN and DRL models in fault identification is presented as 96.12% and 98.04%, respectively. Finally, in the early detection of single-phase to ground short circuit fault location (high impedance), the results revealed the efficiency of the DRL model with R=96.61% compared to CNN with R=95.21%.

When the permanent magnet synchronous motor (PMSM) operates at low carrier ratio, the decoupling capability and dynamic performance of the proportional-integral (PI) controller are limited by the digitization delay. In this paper, a deadbeat PI controller is proposed by modified the feedforward. With the modified feedforward, the open-loop transfer function can eliminate imaginary components. Thereby the full decoupling of $d$ and $q$ is realized. At the same time, the controller also has full control over the location of the closed-loop poles, indicating that it can have a deadbeat response for the command tracking. For the disturbance rejection, the controller allows us to select the speed with which the controller can cancel the effect of a disturbance. Further considering that the motor parameters are inaccurate, by designing the controller coefficients with the maximum value of the closed-loop poles, the current-loop can ensure the dynamic performance and harmonic suppression ability. Finally, the effectiveness of the proposed controller is verified by the simulations and experiments.

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimization algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimization algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimization process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics.

In this paper, impedance modeling of a DC microgrid system consisting of a source and load converter, including an input filter, is performed. Impedance-based modeling has been used to derive mathematical models of the output impedance of the source converter and the input impedance of the load converter. The correlation between the converter interaction and system stability is analyzed based on the mathematical model. An impedance-based stability analysis is used to determine the system stability by analyzing the interactions among the converters in the DC microgrid system. Middlebrook's stability criterion, which uses the impedance transfer function, is applied to determine system stability. Moreover, in this paper, a stability enhancement control algorithm is proposed to resolve the system instabilities resulting from interaction among the converters and the distortion caused by the harmonics emanating from the AC input. The proposed stability enhancement control algorithm consists of a feed-forward type virtual impedance (VI) and a proportional-resonant (PR) controller. The validity of the proposed method is demonstrated by the results of the response characteristics in the frequency domain, and the effectiveness of the proposed control algorithm is verified via simulations and prototype experimental models.

In this paper, a hybrid uniplanar compact electromagnetic bandgap (UC-EBG) design is proposed with a wide-band noise suppression in power planes of printed circuit boards (PCBs). To achieve wide-band noise suppression, the proposed hybrid UC-EBG design employs a combination of various uniplanar EBG unit cells with different sizes which have different frequency ranges of noise suppression corresponding to the size of the cells. In addition, an analytic model of the EBG unit cell composed of cavity resonant model and meander line inductance is proposed and validated by 3-dimensional field simulation and measurement. To demonstrate the proposed design, a heterogeneous group of unit cells with different sizes was selected and the noise transfer function was compared with that of a conventional UC-EBG structure. The proposed hybrid UC-EBG design is experimentally verified by noise transfer function measurement with a significant expansion of the stopband up to 478 % in comparison to the conventional uniplanar EBG designs with the same area occupation.

In this work we present a synthesis method for the design of RF and microwave Active Inductors (AI) based on S-parameters and conformal transformations. The proposed approach allows to study and define the desired AI characteristics directly managing the transfer function of an equivalent two-ports network, thus overcoming the difficulties relative to the characterization of the classical closed-loop architecture that characterizes both the proposed AI architecture and the classical gyrator-C capacitor scheme. By analyzing the obtained AI open-loop transfer function, it is possible to define the design criteria useful to obtain a single port equivalent network showing an inductive behavior at the desired frequency. An example of application of the proposed approach is also provided and the obtained AI is used for the design of a low-power active filter. Measurements are in good agreement with the simulation results, demonstrating the feasibility of the proposed design approach.