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Control-signal-to-output-voltage transfer function of the conventional boost converter has at least one right-half plane zero (RHPZ) in the continuous conduction mode which can restrict the open-loop bandwidth of the converter. This problem can complicate the control design for the load voltage regulation and conversely, impact on the stability of the closed-loop system. To remove this positive zero and improve the dynamic performance, this paper aims to suggest a novel boost topology with a step-up voltage gain by developing the circuit diagram of a conventional boost converter.

Using a transformer, two different pathways are provided for a classical boost circuit. Hence, the effect of the RHPZ can be easily canceled and the voltage gain can be enhanced which provides conditions for achieving a smaller working duty cycle and reducing the voltage stress of the power switch. Using this technique makes it possible to achieve a good dynamic response compared to the classical boost converter.

The observations show that the phase margin of the proposed boost converter can be adequately improved, its bandwidth is largely increased, due to its minimum-phase structure through RHPZ cancellation. It is suitable for fast dynamic response applications such as micro-inverters and fuel cells.

The introduced method is analytically studied via determining the state-space model and necessary criteria are obtained to achieve a minimum-phase structure. Practical observations of a constructed prototype for the voltage conversion from 24 V to 100 V and various load conditions are shown.

The model for digitally controlled three-phase pulse width modulation (PWM) boost rectifiers is a sampled data model, which is different from the continuous time domain models presented in previous studies. The controller, which is tuned according to the model in continuous time domain and discretized by approximation methods, may exhibit some unpredictable performances and even result in unstable systems under some extreme situations. Consequently, a small-signal discrete-time model of digitally controlled three-phase PWM boost rectifier is required. The purpose of this paper is to provide a simple but accurate small-signal discrete-time model of digital controlled three-phase PWM boost rectifier, which explains the effect of the sampling period, modulator and time delays on system dynamic and improves the control performance.

Based on the Laplace domain analysis and the waveforms of up-down-count modulator, the small signal model of digital pulse width modulation (DPWM) in the Laplace domain is presented. With a combination of state-space average and a discrete-time modeling technique, a simplified large signal discrete time model is developed. With rotation transformation and feed-forward decoupling, the large-signal model is decoupled into a single input single output system with rotation transformation. Then, an integrated small signal model in the Laplace domain is constructed that included the time delay and modulation effect. Implementing the modified z-transform, a small-signal discrete-time model is derived from the integrated small signal model.

In a digital control system, besides the circuit parameters, the location of pole of open-loop transfer function is also related to system sampling time, affecting the system stability, and the time delay determines the location of the zero of open-loop transfer function, affecting the system dynamic. In addition to the circuit parameters discussed in previous literature, the right half plane (RHP) zero is also determined by the sampling period and the time delay. Furthermore, the corner frequency of the RHP zero is mainly determined by the sampling period.

The model developed in this paper, accounting for the effect of the sampling period, modulator and time delays on the system dynamic, give a sufficient insight into the behavior of the digitally controlled three-phase PWM rectifier. It can also explain the effect of sampling period and control delay time on system dynamic, accurately predict the system stability boundary and determine the oscillation frequency of the current loop in critical stable. The experimental results verify that the model is a simple and accurate control-oriented small-signal discrete-time model for the digitally controlled three-phase PWM boost rectifier.

Conventional isolated dc–dc converters offer an efficient solution for performing voltage conversion with a large improved voltage gain. However, the small-signal analysis of these converters shows that a right-half-plane (RHP) zero appears in their control-to-output transfer function, exhibiting a nonminimum-phase stability. This RHP zero can limit the frequency response and dynamic specifications of the converters; therefore, the output voltage response is sluggish. To overcome these problems, the purpose of this study is to analyze, model and design a new isolated forward single-ended primary-inductor converter (IFSEPIC) through RHP zero alleviation.

At first, the normal operation of the suggested IFSEPIC is studied. Then, its average model and control-to-output transfer function are derived. Based on the obtained model and Routh–Hurwitz criterion, the components are suitably designed for the proposed IFSEPIC, such that the derived dynamic model can eliminate the RHP zero.

The advantages of the proposed IFSEPIC can be summarized as: This converter can provide conditions to achieve fast dynamic behavior and minimum-phase stability, owing to the RHP zero cancellation; with respect to conventional isolated converters, a larger gain can be realized using the proposed topology; thus, it is possible to attain a smaller operating duty cycle; for conventional isolated converters, transformer core saturation is a major concern, owing to a large magnetizing current. However, the average value of the magnetizing current becomes zero for the proposed IFSEPIC, thereby avoiding core saturation, particularly at high frequencies; and the input current of the proposed converter is continuous, reducing input current ripple.

The key benefits of the proposed IFSEPIC are shown via comparisons. To validate the design method and theoretical findings, a practical implementation is presented.

This paper aims to propose a laser micro-machined 4 × 4 elements microstrip array antenna suitable for 5 G millimeter wave (mm-wave) applications. Each patch element of the array is excited with same amplitude and phase that is achieved with proper novel impedance matching stub. The proposed antenna achieves a simulated gain of 13.15 dBi and a measured return loss of −24.80 dB at 28.73 GHz with a total bandwidth of 7.48 GHz. The designed antenna is directional with a directivity of 15.1 dBi at 28.73 GHz, whereas fabricated on a low cost FR4 substrate with a substrate thickness of 0.074 λ mm. The antenna is realized with an aperture size of 2.24λ × 3.26λ.

The antenna structure starts from the design of single element called unit cell. The single element is designed using the transmission line model equations of a rectangular patch. To design a 28 GHz microstrip patch antenna, a dielectric material with lower permittivity and having thickness (h) less than 1 mm is required. This specification gives better gain and efficiency by reducing surface waves and mutual coupling between elements. The inset width is optimized to achieve the minimum reflection coefficient (S11). The single element has been arranged with a minimum spacing of λ/2 (5.3571 mm) in an H plane and E plane. It is connected using the microstrip lines with proper impedance matching. The four 2 × 2-sub array cell subsystems are connected with a corporate feed together formed the 4 × 4-array cell. Rectangular planar array method is used to arrange the elements in the 4 × 4 array cell.

The design concept is simple which includes the combination of corporate feed and insect feed. It is compact in size and easy to fabricate. The bandwidth of fabricated prototype antenna array is achieved as 7.48 GHz from 24.98 GHz to 32.46 GHz. The mutual coupling is very less though the antenna array is placed with minimum spacing between adjacent elements. This is because of the microstrip feeding structure with minimum phase shift. The gain can be further enhanced with increasing number of array element and proper designing of feed line. Owing to the advantages of low profile, wide bandwidth and high gain, the designed array will be potentially useful in 5 G wireless communications.

The measured antenna offers bandwidth 7.48 GHz (24.98 GHz-32.46 GHz) with centered frequency 28.73 GHz. The agreement between simulated and measured results is good. The VSWR is observed 0.32 < 2, offers good impedance matching and low mutual coupling. It gives better E-Field and H-field radiation patterns of the 4 × 4 array antenna structure at 28 GHz. The total gain of 13.14 dBi is achieved at the center frequency. The total efficiency of 63.42 per cent is achieved with FR4 substrate.

This paper addresses the adaptive passivation of multi‐input multi‐output (MIMO) non‐linear systems,with unknown parameters. The class of MIMO non‐linear systems considered here has an explicit linear parametric uncertainty and it is made equivalent to a passive system by means of an adaptive controller with adaptive laws specially designed, which include suitable time‐varying gains. The solution presented here is an extension of that obtained by the authors for single‐input single‐output (SISO) systems. The proposed algorithm was applied, at simulation level, to models of dynamical MIMO systems, to exemplify the controller design methodology and to observe the adaptive system behavior.

The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion.

This paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects.

Thermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process.

In this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

This paper proposes a useful pseudo‐logarithmic circuit as a basic building block in the construction of a logarithmic amplifier made from piecewise approximations.

The circuit employs two operational and a handful of resistors, and mimics the logarithmic function over a predefined range. Control of the pseudo‐logarithmic function is achieved by a ratio of resistances defined as x, one of which may be digitally switched, or implemented by tunable transconductors. When controlled by digitally switched resistors, the circuit is particularly attractive because of the commercial availability of such resistors, and with the aid of simple control logic, individual blocks can be algebraically summed to extend the dynamic range of the basic pseudo‐logarithmic block which is 28 dB

Experimental results using off the shelf operational amplifiers (opamps) and a handful of resistors show that the circuit yields a maximum log error of 0.6915 dB for x in (0.22, 4.65).

Proposes a novel circuit capable of realizing the pseudo‐logarithmic function (x−1)/(1+x). The circuit is simple and easily implemented using readily available opamps.

The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis.

The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN.

The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning.

The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly.

This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

This paper presents a new surface reconstruction method based on complex form functions, genetic algorithms and neural networks. Surfaces can be reconstructed in an analytical representation format. This representation is optimal in the sense of least‐square fitting by predefined subsets of data points. The surface representations are achieved by evolution via repetitive application of crossover and mutation operations together with a back‐propagation algorithm until a termination condition is met. The expression is finally classified into specific combinations of basic functions. The proposed method can be used for CAD model reconstruction of 3D objects and free smooth shape modelling. We have implemented the system demonstration with Visual C++ and MatLab to enable real time surface visualisation in the process of design.