Stability and performance comparison analysis for linear active disturbance rejection control–based system

This paper investigates the stability and performance of the linear active disturbance rejection control (LADRC)–based system with uncertainties and external disturbance via transfer functions and a frequency-domain view. The performance of LADRC is compared with the state-observer-based state feedback control (SOSFC) and state feedback control (SFC). First, the transfer functions and the error transfer functions for LADRC, SOSFC, and SFC are studied using the state-space method. It is proven that the LADRC-, SOSFC-, and SFC-based closed-loop systems have the same transfer function from the reference input to the output and achieve the same control effects for the nominal system. Then, it is proven for the first time that the LADRC has a better anti-interference ability than the SOSFC and SFC. Besides, the asymptotic stability condition of LADRC-based closed-loop system considering large parameter perturbations is given first. Moreover, the sensitivity analysis of the closed-loop system is carried out. The results show that the LADRC has stronger robustness under parameter perturbations. According to the results, we conclude that the LADRC is of great disturbance rejection ability and strong robustness.

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

Analysis of Non-Minimum Phase System for AC/DC Battery Charger Power Factor Correction Converter

Electric mobility is nowadays one of the more important trends regarding pollution reduction and global warming due to fuel consumption. Big efforts are done in order to develop efficient and reliable power electronic systems for electric vehicles. In two stage on board-battery chargers, one way of improving efficiency is by means of ensuring the DC-DC isolated converter always operates in the nominal input/output voltage ratio, that could be achieved with a variable DC-link operation. In this paper, a four-switch buck-boost based AC/DC converter is deeply analyzed in order to improve its dynamic performance, the power factor and the total harmonic distortion. The converter suffers from a non-minimum phase characteristic in different input–output transfer functions, which reduces the closed-loop bandwidth of the system. Therefore, after a deep converter analysis has been done, different solutions have been evaluated and tested. Finally, a control to different output transfer functions of the converter become minimum phase, which allows us to increase the system bandwidth and, consequently, high power factor, low harmonics distortion, single control structure and fast dynamics for wide output voltage range are achieved.

Characterization of tandem aerosol classifiers for selecting particles: implication for eliminating multiple charging effect

Accurate particle classification plays a vital role in aerosol studies. Differential mobility analyzer (DMA), centrifugal particle mass analyzer (CPMA) and aerodynamic aerosol classifier (AAC) are commonly used to select particles with a specific size or mass. However, multiple charging effect cannot be entirely avoided either using individual technique or using tandem system such as DMA-CPMA, especially when selecting soot particles with fractal structures. In this study, we demonstrate the transfer functions of DMA-CPMA and DMA-AAC systems, as well as the potential multiple charging effect. Our results show that the ability to remove multiply charged particles mainly depends on particles morphology and instruments setups of DMA-CPMA system. Using measurements from soot experiments and literature data, a general trend in the appearance of multiple charging effect with decreasing size when selecting aspherical particles was observed. Otherwise, our results indicated that the ability of DMA-AAC to resolve particles with multiple charges is mainly related to the resolutions of classifiers. In most cases, DMA-AAC can eliminate multiple charging effect regardless of the particle morphology, while particles with multiple charges can be selected when decreasing resolutions of DMA and AAC. We propose that the multiple charging effect should be reconsidered when using DMA-CPMA or DMA-AAC system in estimating size and mass resolved optical properties in the field and lab experiments.

Alpha particle microdosimetry calculations using a shallow neural network

A shallow neural network was trained to accurately calculate the microdosimetric parameters, <z1> and <z1 2> (the first and second moments of the single-event specific energy spectra, respectively) for use in alpha-particle microdosimetry calculations. The regression network of four inputs and two outputs was created in MATLAB and trained on a data set consisting of both previously published microdosimetric data and recent Monte Carlo simulations. The input data consisted of the alpha-particle energies (3.97–8.78 MeV), cell nuclei radii (2–10 µm), cell radii (2.5–20 µm), and eight different source-target configurations. These configurations included both single cells in suspension and cells in geometric clusters. The mean square error (MSE) was used to measure the performance of the network. The sizes of the hidden layers were chosen to minimize MSE without overfitting. The final neural network consisted of two hidden layers with 13 and 20 nodes, respectively, each with tangential sigmoid transfer functions, and was trained on 1932 data points. The overall training/validation resulted in a MSE = 3.71×10-7. A separate testing data set included input values that were not seen by the trained network. The final test on 892 separate data points resulted in a MSE = 2.80×10-7. The 95th percentile testing data errors were within ±1.4% for <z1> outputs and ±2.8% for <z1 2> outputs, respectively. Cell survival was also predicted using actual vs. neural network generated microdosimetric moments and showed overall agreement within ±3.5%. In summary, this trained neural network can accurately produce microdosimetric parameters used for the study of alpha-particle emitters. The network can be exported and shared for tests on independent data sets and new calculations.

Evaluation of a Semi-Airborne Electromagnetic Survey Based on a Multicopter Aircraft System

The semi-airborne electromagnetic (EM) method has the potential to reach deeper exploration depths than purely airborne EM approaches. The concept of the method is to deploy high-power transmitters on the ground, which excite subsurface currents and induce strong magnetic fields, and to measure the corresponding EM fields with a passive airborne receiver instrument. Following recent conceptual developments of the semi-airborne EM technique deployed on helicopters, we performed a 10 km2 semi-airborne EM survey near Münster (Germany) based on a multicopter aircraft system. For this purpose, horizontal electric dipole (HED) transmitters were installed in the survey area and were surveyed individually. Magnetic transfer functions were determined and a model of the conductivity of the study area was derived. Despite restrictions such as low payload capacity and multicopter-related EM noise, we were able to estimate spatially and spectrally consistent transfer functions of high quality up to a distance of 2 km from the respective transmitter. Our results could be validated with independent results from a magnetotelluric and a direct current sounding. The study demonstrates that an unmanned aircraft system (UAS) is suitable for semi-airborne EM application and that such a system can be beneficial where ground-based methods and manned techniques become impractical.

Mathematical Modelling of Twin -T Notch Filter using Floating Admittance Matrix

A novel article presents the RC-notch filter function using the floating admittance matrix approach. The main advantages of the approach underlined the easy implementation and effective computation. The proposed floating admittance matrix (FAM) method is unique, and the same can be used for all types of electronic circuits. This method takes advantage of the partitioning technique for a large network. The sum property of all the elements of any row or any column equal to zero provides the assurance to proceed further for analysis or re-observe the very first equation at the first instant itself. This saves time and energy. The FAM method presented here is so simple that anybody with slight knowledge of electronics but understating the matrix maneuvering can analyze any circuit to derive all types of transfer functions. The mathematical modelling using the FAM method allows the designer to adjust their design at any stage of analysis comfortably. These statements provide compelling reasons for the adoption of the proposed process and demonstrate its benefits.

An Angle-Compensating, Complex-Coefficient PI Controller Used for Decoupling Control of a Permanent-Magnet Synchronous Motor

An asymmetric, cross-coupling effect, as well as digital control delays, in a permanent-magnet synchronous motor (PMSM) will deteriorate its current-control performance in the high-speed range, especially for electric motors used in electric vehicles (EVs) with features such as high-power density and a low carrier/modulation frequency ratio. In this paper, an angle-compensating, complex-coefficient, proportional-integrator (ACCC-PI) controller is proposed, which aims to provide an excellent decoupling performance even with considerable digital control delay. Firstly, the current open and closed loop complex-coefficient transfer functions were established in the synchronous rotation coordinate system. The proposed method, along with existing ones, were then evaluated and theoretically compared. On this basis, the parameter-tuning method of the ACCC-PI controller was presented. Finally, simulation and experimental results proved the correctness of the theoretical analysis and the proposed method.

Development of a seismic site-response zonation map for the Netherlands

Earthquake site response is an essential part of seismic hazard assessment, especially in densely populated areas. The shallow geology of the Netherlands consists of a very heterogeneous soft sediment cover, which has a strong effect on the amplitude of ground shaking. Even though the Netherlands is a low- to moderate-seismicity area, the seismic risk cannot be neglected, in particular, because shallow induced earthquakes occur. The aim of this study is to establish a nationwide site-response zonation by combining 3D lithostratigraphic models and earthquake and ambient vibration recordings. As a first step, we constrain the parameters (velocity contrast and shear-wave velocity) that are indicative of ground motion amplification in the Groningen area. For this, we compare ambient vibration and earthquake recordings using the horizontal-to-vertical spectral ratio (HVSR) method, borehole empirical transfer functions (ETFs), and amplification factors (AFs). This enables us to define an empirical relationship between the amplification measured from earthquakes by using the ETF and AF and the amplification estimated from ambient vibrations by using the HVSR. With this, we show that the HVSR can be used as a first proxy for site response. Subsequently, HVSR curves throughout the Netherlands are estimated. The HVSR amplitude characteristics largely coincide with the in situ lithostratigraphic sequences and the presence of a strong velocity contrast in the near surface. Next, sediment profiles representing the Dutch shallow subsurface are categorised into five classes, where each class represents a level of expected amplification. The mean amplification for each class, and its variability, is quantified using 66 sites with measured earthquake amplification (ETF and AF) and 115 sites with HVSR curves. The site-response (amplification) zonation map for the Netherlands is designed by transforming geological 3D grid cell models into the five classes, and an AF is assigned to most of the classes. This site-response assessment, presented on a nationwide scale, is important for a first identification of regions with increased seismic hazard potential, for example at locations with mining or geothermal energy activities.