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A Self-Adaptive Control for Phase-Controlled Electromagnetic Contactor Using Weighted Moving Average Filter

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第9期68卷 8963-8972页

作者： Fang, Shuhua Chen, Yong Lin, Heyun Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This article proposes a self-adaptive control strategy for controlling the holding current of a novel phase-controlled contactor. The closing moment of contact can be captured by calculating the drop slope of current and the pulsewidth modulation is then applied to decrease the holding current. The weighted moving average filter is employed to filter the current and voltage signals. A novel approach using a quintic B-spline curve-fitting algorithm is proposed to determine the number of sampling values and their weights. Numerous theoretical analyses of electromagnetic force and magnetic flux density distributions are developed. A cosimulation coupling magnetic field, machinery, and circuit are carried out with the Maxwell and circuit editor software. A prototype is manufactured and the experimental results validate the effectiveness of the proposed self-adaptive control strategy for minimizing the holding current.

关键词： Actuators Splines (mathematics) Electromagnetics Pulse width modulation Iron Contacts filtering algorithms Phase controlled contactor pulsewidth modulation (PWM) self-adaptive control strategy weighted moving average filter

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Distributed Kalman Filters With Random Sensor Activation and Noisy Channels

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IEEE SENSORS JOURNAL 2021年第24期21卷 27659-27675页

作者： Jin, Hao Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin 150080 Peoples R China

This paper studies distributed Kalman filtering problems for sensor networks with random sensor activation and noisy channels. Each sensor measures the target state with a random activation and receives state estimates of its neighbor nodes. Communication noises are considered in communication links between sensors and their neighbor nodes. Considering cross-covariance matrices between sensors, an optimal distributed Kalman filter (ODKF) with a given recursive structure is proposed for each sensor, which depends on the knowledge whether a sensor itself is activated to measure the target state or not and whose filter gains and covariance matrices depend on random activation rate of the sensor. In the ODKF algorithm, optimal Kalman filter gain for each sensor and different optimal consensus filter gains for state estimates of its neighbor nodes are designed in the linear unbiased minimum variance (LUMV) sense. To reduce computational cost, a suboptimal distributed Kalman filter (SDKF) is also presented by considering a locally minimum upper bound of the filtering error covariance matrix, where the calculation of cross-covariance matrices among sensor nodes is avoided. The stability and steady-state property of the proposed ODKF and SDKF algorithms are explored. Finally, the proposed methods are also extended to the nonlinear scenarios. Simulation examples show the effectiveness of the proposed algorithms.

关键词： Sensors Kalman filters Covariance matrices Estimation filtering algorithms Adaptive filters Sensor fusion Distributed Kalman filter random sensor activation noisy channels sensor network steady-state property

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An Optimal Transport Formulation of the Ensemble Kalman Filter

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第7期66卷 3052-3067页

作者： Taghvaei, Amirhossein Mehta, Prashant G. Univ Illinois Urbana Champaign UIUC Champaign IL 61820 USA Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA Univ Illinois Urbana Champaign UIUC Coordinated Sci Lab Champaign IL 61820 USA Univ Illinois Urbana Champaign UIUC Dept Mech Sci & Engn Champaign IL 61820 USA

Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the nonlinear filtering problem in continuous time. The distinguishing feature of these algorithms is that the Bayesian update step is implemented using a feedback control law. It has been noted in the literature that the control law is not unique. This is the main problem addressed in this article. To obtain a unique control law, the filtering problem is formulated here as an optimal transportation problem. An explicit formula for the (mean-field type) optimal control law is derived in the linear Gaussian setting. Comparisons are made with the control laws for different types of EnKF algorithms described in the literature. Via empirical approximation of the mean-field control law, a finite-N controlled interacting particle algorithm is obtained. For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter. This allows strong conclusions on convergence and error properties based on the classical filter stability theory for the Kalman filter. It is shown that, under certain technical conditions, the mean squared error converges to zero even with a finite number of particles. A detailed propagation of chaos analysis is carried out for the finite-N algorithm. The analysis is used to prove weak convergence of the empirical distribution as N -> infinity. For a certain simplified filtering problem, analytical comparison of the mse with the importance sampling-based algorithms is described. The analysis helps explain the favorable scaling properties of the control-based algorithms reported in several numerical studies in recent literature.

关键词： filtering algorithms Kalman filter Monte Carlo methods stochastic processes

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FxRLS algorithms Based Active Control of Impulsive Noise With Online Secondary Path Modeling

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IEEE ACCESS 2021年 9卷 117471-117485页

作者： Jabeen, Fareeha Mirza, Alina Zeb, Ayesha Imran, Muhammad Afzal, Farkhanda Maqbool, Ayesha Natl Univ Sci & Technol NUST MCS Islamabad 44000 Pakistan Natl Univ Sci & Technol NUST CEME Islamabad 44000 Pakistan Natl Univ Sci & Technol NUST NBC Islamabad 44000 Pakistan

Active noise control algorithms undergo stability problems in the presence of impulsive noise. This paper investigates such algorithms with online secondary path modeling for impulsive noise and varying acoustic paths. The paper presents three methods for active noise control, along with improved online secondary path modeling. Firstly, the filtered x recursive least square algorithm is applied for both active noise control and online secondary path modeling. This method gave faster convergence, improved stability, and modeling accuracy as compared to existing ones. The filtered x recursive least square algorithm is not robust for abruptly changing acoustic paths. To overcome this problem another method that uses modified gain filtered x recursive least square algorithm for active noise control is presented. Furthermore, it is observed that modified gain filtered x recursive least square achieves the desired performance with overheads of increased complexity. Thus, a hybrid method is proposed which has less computational complexity than the rest methods with no compromise on active noise control system performance.

关键词： Adaptive filters filtering algorithms Convergence Computational modeling Computational complexity Acoustics Robustness Active noise control path modeling least square algorithms computational complexity

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A Method of D-Type Weld Seam Extraction Based on Point Clouds

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IEEE ACCESS 2021年 9卷 65401-65410页

作者： Gao, Jie Li, Fang Zhang, Cong He, Wenhao He, Jinhu Chen, Xubing Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China

Identification of the seam to be welded in automatic welding is an important challenge, especially on a complex 3D surface. At present, most studies on weld extraction are for butt joints and fillet joints. Few studies focus on D-type welds, which connect to pipelines or pressure vessels. In this paper, a method of identification and extraction of pipeline joint welds based on 3D point clouds is proposed. First, the problem that the surface of pipe fittings cannot be effectively stitched owing to lack of markings is solved by putting marker points on the surface to allow accurate splicing of the point cloud. Second, according to the contour feature information of the workpiece, a processing method for D-type welding seams is proposed. Finally, a boundary extraction algorithm based on the point cloud normal is used to obtain the point cloud of the weld. The experimental results show that this method can effectively extract the weld. The running time for the point cloud weld extraction method is less than half that of the existing methods. The accuracy of the proposed method is improved in all three directions: by 28.2% in the X direction, 19.1% in the Y direction and 16.0% in the Z direction. Moreover, this method is convenient for transforming weld coordinates to robot-based coordinates for the automatic welding of spatially complex welds.

关键词： Three-dimensional displays Welding Feature extraction filtering algorithms filtering Splicing Data mining D type weld seam feature extraction point cloud automated welding

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Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

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IEEE SENSORS JOURNAL 2021年第3期21卷 3408-3419页

作者： Caruso, Marco Sabatini, Angelo Maria Knaflitz, Marco Gazzoni, Marco Della Croce, Ugo Cereatti, Andrea Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Scuola Super Sant Anna BioRobot Inst I-56127 Pisa Italy Scuola Super Sant Anna Dept Excellence & AI Robot I-56127 Pisa Italy Univ Sassari Dept Biomed Sci I-07100 Sassari Italy Univ Sassari Human Neuromuscoloskeletal Syst Interunivers Ctr Bioengn I-07100 Sassari Italy

Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial- and- error (or grid- search) approach to find the optimal values. However, using trial- and- error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this article, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial- and- error were coincident, while in the other cases the maximumdiscrepancy amounted to 2.5 deg and less than 1.5 deg on average.

关键词： Magnetic separation Sensor fusion Optical filters Magnetometers Accelerometers filtering algorithms MIMU filter parameter wearable sensors gait analysis

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Multi-Fault Diagnosis of Interacting Multiple Model Batteries Based on Low Inertia Noise Reduction

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IEEE ACCESS 2021年 9卷 18465-18480页

作者： Wang, Yongchao Meng, Dawei Li, Ran Zhou, Yongqin Zhang, Xiaoyu Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin 150080 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300071 Peoples R China

In view of the problems of declined estimation and diagnostic accuracy, as well as diagnosis delay caused by the fixed model transformation probability of the interacting multiple model (IMM) fault diagnosis algorithm, an IMM algorithm based on low inertia noise reduction (LN-IMM) was presented in this paper. The proposed algorithm realized the multi-fault diagnosis of lithium-ion batteries in combination with strong tracking Kalman filter (STKF). In the non-model transformation stage, a transition probability correction function was constructed using the difference of the n-th order based on model probability to suppress the effect of noise on the estimation accuracy of the algorithm. In the model transformation stage, a model jump threshold was introduced in order to reduce inertia when the matched model was switched and realized quick model transformation. Accordingly, the final experiment proved that the LN-IMM-STKF algorithm efficiently completed the state estimation of lithium-ion batteries and improved the diagnostic accuracy of faults while reducing diagnosis delay and attaining accurate detection as well as rapid separation of battery fault information.

关键词： Batteries Circuit faults Fault diagnosis Integrated circuit modeling Switches Kalman filters filtering algorithms Interactive multiple model low inertia noise reduction strong tracking Kalman filter lithium-ion battery

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Comparison of the constant prediction time of collaborative filtering algorithms by using time contexts

Comparison of the constant prediction time of collaborative ...

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International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sumet Darapisut Jakkarin Suksawatchon Informatics Burapha University Chonburi Thailand

ISBN: (纸本)9781479958238

This research presents the comparison of collaborative filtering techniques which are Tendencies Based Algorithm, Item mean algorithm, and Simple mean based algorithm. All these algorithms use the constant time in prediction process. To evaluate our proposed model, we use *** dataset including music listening history of each user. Each user's profile is split into several sub-profiles based on specified time ranges called “Time Contexts”. Thus the prediction is done using these Time Contexts instead of a single user profile. From our experiments, we have found that Tendencies Based Algorithm with Time Contexts is effective. It is given more accuracy and much more efficient computationally than tradition collaborative filtering algorithms.

关键词： Prediction algorithms Collaboration Music Recommender systems Mathematical model filtering algorithms

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A Third-Order MAF Based QT1-PLL That is Robust Against Harmonically Distorted Grid Voltage With Frequency Deviation

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2021年第3期36卷 1600-1613页

作者： Mellouli, Mohamed Hamouda, Mahmoud Slama, Jaleleddine Ben Hadj Al-Haddad, Kamal Univ Sousse Res Lab LATIS ENISO Sousse 4023 Tunisia ETS Montreal Canada Res Chair Energy Convers & Power Elect Montreal PQ H3C 1K3 Canada

The quasi-type-l phase-locked loop (QT1-PLL) is a grid synchronization technique that has become very popular in recent years thanks to its attractive performance such as easy implementation, fast dynamic response, and good accuracy in steady-state operation. However, it is still vulnerable to operation under harmonically distorted grid voltages with frequency drift. This paper proposes a novel QT1-PLL based synchronization algorithm that makes an appropriate combination of two filters' types: an in-loop third-order moving average filter (MAF) with a reduced window width, and a simplified second-order fast delayed signal cancellation (FDSC) based prefiltering stage. The proposed PLL is named third-order MAF based QT1-PLL (TQT1-PLL). Though both TQT1-PLL's filters do not need any adaptive algorithm, it is able to reject non-triplen odd-harmonics and the fundamental frequency negative sequence (FFNS) even under grid frequency drift. Its correct operation is confirmed through numerical simulations and real-time implementation on a digital signal processor (DSP). Moreover, the obtained results confirm its ability to reduce the ripple in the estimated frequency and phase under distorted grid voltages and off-nominal frequency operation. Authors show also through an analytical development that the topology of the proposed TQT1-PLL can be extended to enable the rejection of the DC-offset.

关键词： Phase locked loops Power harmonic filters Harmonic distortion Harmonic analysis filtering algorithms Frequency estimation Synchronization Delayed signal cancellation (DSC) distributed power generation harmonic distortion moving average filter (MAF) phase-locked loop (PLL) power quality stability

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Algorithm Allows Changing the Required Voice to a Predetermined One Using a Surrogate Voice

Algorithm Allows Changing the Required Voice to a Predetermi...

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2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2022

作者： Afonichkina, Polina Y. Silnov, Dmitry S. Leonova, Natalia M. Modyaev, Alexey D. Moscow Russia

ISBN: (纸本)9781665409933

This article describes the algorithm for changing the input voice to a predefined voice. The main principle and new principle of the algorithm is the creation of a surrogate voice, the use of which will significantly reduce the time spent on the operation of the algorithm. The work consists of 3 main stages: training the neural network model according to the given parameters of the initial voice;calculation of filters that affect the characteristics of speech: tempo, speed, intonation, defects, as well as musical characteristics;applying the received filters to the input - variable voice. © 2022 IEEE.

关键词： Training Neural networks Music filtering algorithms

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