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Discrete Current Controller for SPMSMs at Low Sampling-to-Fundamental Frequency Ratios using Critical Damping Feedback

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IEEE Transactions on Power Electronics 2025年第8期40卷 11787-11798页

作者： Huang, Zhen Zhu, Rui Zhang, Yun Zanchetta, Pericle Nanchang University School of Information Engineering Nanchang330000 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Pavia Electrical Computer and Biomedical Engineering Department Pavia27100 Italy

The discrete current controllers can generally ensure high-performance machine control. However, for high-speed machines associated with low sampling-to-fundamental frequency ratios (SFR), the unavoidable disturbances now will seriously affect the control performance. To address this issue, an advanced discrete current controller is proposed in this paper. The critical damping feedback is used to achieve the maximum damping ratio of the disturbance loop at any SFR. Compared with the existing discrete controllers, the proposed one can significantly reduce the impact of multiple parameter mismatches on the performance of the controller while improving the current tracking response and enhancing the anti-disturbance capability. Finally, the experimental results on a 10krpm high-speed permanent magnet synchronous machine with the lowest SFR of 8 have proven the feasibility and advantages of the addressed current controller. © 1986-2012 IEEE.

关键词： Damping

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A Hybrid P&O-Fuzzy-Based Maximum Power Point Tracking (MPPT) Algorithm for Photovoltaic Systems under Partial Shading Conditions

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IEEE Access 2025年 13卷 86046-86056页

作者： Karimi, Hamed Siadatan, Alireza Rezaei-Zare, Afshin Khatam University Department of Electrical Engineering Tehran Iran York University Department of Electrical Engineering and Computer Science Toronto Canada

Photovoltaic arrays receive varying levels of solar radiation due to factors such as shadows created by clouds, surrounding buildings, and other obstructions. Therefore, an effective Maximum Power Point Tracking (MPPT) algorithm should be used to maximize power generation from non-uniform arrays. Most MPPT algorithms do not perform well during partial shadow conditions, as they can become trapped in local maxima and fail to identify the absolute global maximum point (GP). This paper proposes a new MPPT approach that combines Perturb & Observe (P&O) and Fuzzy Logic Controller (FLC) with the Particle Swarm Optimization (PSO) algorithm. The FLC algorithm is then used to maximize search accuracy. The configurations of this system and the study results demonstrate the dynamic and promising performance of the proposed method under various environmental and shading conditions. In addition to its simplicity and ease of implementation, the proposed algorithm exhibits high accuracy in tracking the maximum power point. This has been evidenced through both simulation and laboratory results of the hardware. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Label Consistent Generalized Adaptive Weighted Recursive Lea...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Yousefi, Mohadeseh Naderahmadian, Yashar Beheshti, Soosan University of Guilan Department of Electrical and Computer Engineering Rasht Iran Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798331506520

The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class. © 2025 IEEE.

关键词： Self-supervised learning

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A stabilizing solution for the control system parameters tuning problem

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ISA Transactions 2025年 161卷 261-275页

作者： Rehimi, Sharara Bevrani, Hassan Urabe, Chiyori T. Kato, Takeyoshi Department of Electrical Engineering Nagoya University Nagoya Japan Department of Electrical & Computer Engineering University of Kurdistan Kurdistan Sanandaj Iran

Dynamic control systems present significant challenges in parameter tuning due to their complex behaviors, various performance requirements, and wide application domains. Existing strategies often rely on restrictive assumptions or are obtained sequentially, resulting in poor stability and performance. To overcome these limitations, this study introduces a developed static output feedback framework that stabilizes and simultaneously tunes multiple parameters in various dynamic control systems. The proposed methodology combines a systematic analytical procedure with a software-based implementation, enabling proper performance under varying system types, orders, and parameter sets. Indeed, the introduced tuning approach enables the simultaneous adjustment of all control system parameters, accommodating the designer's preferences regarding the number of parameters to be tuned and the control objectives. Its effectiveness and efficiency are demonstrated through extensive simulations and real-time experiments in aerospace, robotics, and electric systems. Results indicate that this approach covers significant system variations without compromising stability or performance, distinguishing it from the conventional tuning methods. These outcomes offer new insights into the importance of simultaneously retuning control system parameters due to technical and economic issues. It is worth emphasizing that the proposed approach provides a comprehensive plan for designing or tuning controller parameters simultaneously and extends the applicability of static output feedback control theory to a wider range of dynamic control systems. © 2025 The Authors

关键词： Feedback control

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Derandomizing Codes for the Adversarial Wiretap Channel of Type II

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IEEE Transactions on Information Theory 2025年第6期71卷 4686-4707页

作者： Ruzomberka, Eric Nikbakht, Homa Brinton, Christopher G. Love, David J. Poor, H. Vincent Air Force Institute of Technology Department of Electrical and Computer Engineering United States Princeton University Department of Electrical and Computer Engineering United States Purdue University Elmore Family School of Electrical and Computer Engineering West Lafayette United States

The adversarial wiretap channel of type II (AWTC-II) is a communication channel that can a) read a fraction of the transmitted symbols up to a given bound and b) induce both errors and erasures in a fraction of the symbols up to given bounds. The channel is controlled by an adversary who can freely choose the locations of the symbol reads, errors and erasures via a process with unbounded computational power. The AWTC-II is an extension of Ozarow’s and Wyner’s wiretap channel of type II to the adversarial channel setting. The semantic-secrecy (SS) capacity of the AWTC-II is partially known, where the best-known lower bound is non-constructive and proven via a random coding argument that uses a large number (that is, exponential in blocklength n) of random bits to describe the random code. In this work, we establish a new derandomization result in which we match the best-known lower bound via a non-constructive random code that uses only O(n2) random bits. Unlike fully random codes, our derandomized code admits an efficient encoding algorithm and benefits from some linear structure. Our derandomization result is a novel application of random pseudolinear codes – a class of non-linear codes first proposed for applications outside the AWTC-II setting, which have k-wise independent codewords where k is a design parameter. As the key technical tool in our analysis, we provide a novel concentration inequality for sums of random variables with limited independence, as well as a soft-covering lemma similar to that of Goldfeld, Cuff and Permuter that holds for random codes with k-wise independent codewords. © 1963-2012 IEEE.

关键词： Signal encoding

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Semi-Supervised Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Semi-Supervised Generalized Adaptive Weighted Recursive Leas...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

ISBN: (纸本)9798331506520

This work presents a novel semi-supervised dictionary learning framework that updates the dictionary by online learning and is efficient in utilizing the training data. The method employs a two-stage process to train the dictionary: initial training with limited labeled data, followed by online refinement using abundant unlabeled data. We introduce an adaptive correction weight to control the influence of new unlabeled data on the dictionary update based on its consistency with the current model estimate. This approach enables efficient use of the training data set. Moreover, results in faster dictionary convergence and improves data representation accuracy, especially in scenarios with limited training data. Experimental results demonstrate significant enhancement in the classification accuracy of the proposed method compared to the state-of-the-art semi-supervised dictionary learning methods, particularly when dealing with a limited number of training samples. © 2025 IEEE.

关键词： Self-supervised learning

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A Remediation Framework against False Data Injection Cyberattacks Targeting ULTC Transformers to Avoid Voltage Collapse in Smart Distribution Networks

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IEEE Transactions on Industry Applications 2025年第3期61卷 5148-5160页

作者： Naderi, Ehsan Asrari, Arash Arkansas State University Department of Electrical Engineering College of Engineering and Computer Science JonesboroAR United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States

False data injection (FDI) attacks targeting under-load tap changing (ULTC) transformers pose a significant threat to smart distribution networks by exploiting vulnerabilities in the volt-var optimization (VVO) process, leading to potential undervoltage and voltage collapse. The increased integration of renewable energy and cyber-physical systems has expanded the attack surface, making traditional detection methods inadequate. For example, in 2023, attacks on utilities and decentralized components in the United States rose by 200%, with overall cyber threats increasing by 104%, highlighting growing vulnerabilities in distribution systems. To this end, this article proposes a two-stage remediation framework for decentralized FDI (DFDI) attacks targeting ULTC transformers. In the attack stage, vulnerabilities in ULTCs and voltage regulators are scrutinized, risking voltage collapse or blackouts in the distribution system. In the remediation stage, the distribution system operator focuses on non-attacked ULTCs, voltage regulators, distributed generation (DG) units, and smart homes to minimize reliance on compromised components. In this regard, a distinctive formulation of distribution network resilience and load management (DNRLM) problem is introduced to identify a resilient network topology and determine a situational power balance strategy. The proposed framework focuses on minimizing the system's reliance on the attacked ULTCs and voltage regulator components, thereby avoiding the intended voltage collapse caused by such DFDIs. The simulation results verify that the proposed method reduces the voltage collapse proximity index by over 60%, enhancing system resilience under DFDI attacks. © 2025 IEEE.

关键词： Distribution transformers

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Supervised Learning for Real-time Model Predictive Control Leveraging Multi-parametric Mixed Integer Quadratic Programming

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Journal of Institute of Control, Robotics and Systems 2025年第2期31卷 98-105页

作者： Yoo, Seungjun Gwon, Minwoo Kim, Kwang-Ki Department of Electrical and Computer Engineering Inha University Korea Republic of

This paper presents a novel supervised learning framework for real-time optimization of multi-parametric mixed-integer quadratic programming (mp-MIQP) problems. The framework utilizes a multi-layer perceptron (MLP) model to efficiently predict both continuous and binary control inputs while classifying the feasibility of the optimization problem. To address the computational burden of branch-and-bound methods and the memory limitations of explicit model predictive control (MPC), this framework learns optimal control inputs across diverse system states and prediction horizons through offline training. Comparative evaluations reveal that the proposed method significantly outperforms traditional approaches in computational speed and memory efficiency while maintaining high accuracy in approximating optimal solutions. These advancements position the framework as a transformative solution for real-time control applications, offering a robust and efficient alternative for online optimization in mp-MIQP problems. © ICROS 2025.

关键词： Mixed-integer linear programming

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Semantic Segmentation Using an Improved ResNet Structure and Efficient Channel Attention Mechanism Applied to Atrous Spatial Pyramid Pooling in a Fully Convolutional Network

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International Journal of engineering, Transactions B: Applications 2025年第11期38卷 2511-2526页

作者： Sahragard, E. Farsi, H. Mohamadzadeh, S. Department of Electrical and Computer Engineering University of Birjand Birjand Iran

In the field of computer vision, semantic segmentation became an important problem that has applications in fields such as autonomous driving and robotics. Image segmentation datasets, on the other hand, present substantial hurdles due to the high intra-class variability, which includes differences across car models or building designs, and the low inter-class variability, which makes it difficult to discern between objects such as buildings that have facades that are visually identical. A focus-enhanced ASPP module that is coupled with an upgraded backbone for semantic segmentation networks is presented in this study as a solution to the problems that have been identified. In order to augment the adaptability of extracted features, the proposed framework utilizes the capability of an attention ASPP module to implement attention processes within the multiscale module. In order to efficiently capture complex features, the encoder stage also makes use of a ResNet-50 backbone that has been properly optimized. In addition, to increase the robustness of the model, data augmentation approaches are applied. mDice of 87.82, mIoU of 79.05, and mean accuracy of 85.2 on the Stanford dataset, and mDice of 88.91, mIoU of 80.03, and mean accuracy of 89.84 on the Cityscapes dataset, according to experimental assessments, demonstrate that the developed technique performs at an accuracy level that is believed to be modern. As a result of these findings, the possibility for greatly improving semantic segmentation performance may be highlighted by integrating attention mechanisms, ASPP modules, and upgraded ResNet structures. ©2025 The author(s).

关键词： Semantic Segmentation

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Gaussian Q Function Approximation in Wireless Communication System’s Design: A Gradient-Based Optimization Approach

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IEEE Access 2025年 13卷 80958-80970页

作者： Ranchhodbhai, Leuva Bhumika Sadhwani, Dharmendra Department of Electrical and Computer Science Engineering Ahmedabad380026 India

This paper proposes a novel iterative gradient-based optimization approach aimed at achieving more precise and streamlined approximations for the Gaussian Q function—an essential element in communication system’s design. Our optimization strategy involves determining optimal weights for exponential functions within a defined range of the argument (x) of the Gaussian Q function. The primary goal is to iteratively minimize the error or cost function, progressively refining local minima across successive iterations. The results strikingly highlight the exceptional accuracy of our proposed approach in approximating the Gaussian Q function, positioning it as a robust contender for real-world communication system’s design. Importantly, our method showcases a clear superiority in accuracy over prevailing techniques. Moreover, to comprehensively evaluate the efficacy of our approach, we simplify the key metrics like symbol error probability (SEP) of various coherent digital modulation techniques over a versatile multi-cluster fluctuating two-ray fading model, which includes majority of the classical fading models like Rayleigh, Nakagami- m,q , Rician, Rician-shadowed, fluctuating two-wave, two-wave with diffused power, etc. As an application example, the numerical results of the SEP of square quadrature amplitude modulation (SQAM) are validated against the exact results obtained via MATHEMATICA software package. In addition, a comparative analysis on the time required to compute the exact and proposed SEP values is also highlighted in this paper. The asymptotic SEP is also derived which gives an idea on the diversity order of the system. Lastly, extensive Monte-Carlo simulations have also been carried out to validate the proposed work. © 2013 IEEE.

关键词： Cost functions

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