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Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

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Neural Computing and Applications 2024年第18期36卷 10881-10896页

作者： Soydemir, Mehmet Uğur Şahin, Savaş Kocaoğlu, Aykut Bulucu, Parvin Güzeliş, Cüneyt Department of Electrical and Electronics Engineering İzmir Katip Çelebi University İzmir Turkey Department of Electrical and Energy İzmir Vocational School Dokuz Eylül University İzmir Turkey Department of Electrical and Electronics Engineering Graduate School Yaşar University İzmir Turkey Department of Electrical and Electronics Engineering Yaşar University İzmir Turkey

This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as 1,Ε, namely Ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error. © The Author(s) 2024.

关键词： Feedback linearization

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Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

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Computer Systems Science & engineering 2023年第1期44卷 367-390页

作者： S.Meenakshi Ammal P.S.Manoharan Department of Electrical and Electronics Engineering Thiagarajar College of EngineeringMadurai625015Tamil NaduIndia

Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD *** these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next *** proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using *** the proposed work,a publicly available dataset collected using wearables is ***,no real-world data is available to illustrate the daily activities of ***,the proposed method has synthesized the wearables data according to the abnormal activities of *** the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal *** results obtained show that the proposed MH architectures achieve improved performance than the conventional ***,the MH models for activity recognition perform better than the abnormality detection.

关键词： Alzheimer’s disease abnormal activity detection classifier chain multi-headed CNN-LSTM wearable sensor

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Binary feature mask optimization for feature selection

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Neural Computing and Applications 2025年第6期37卷 5155-5167页

作者： Lorasdagi, Mehmet E. Turali, Mehmet Y. Kozat, Suleyman S. Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles United States

We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s

关键词： Mean square error

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Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

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Energy engineering 2023年第8期120卷 1747-1761页

作者： Nisha Vasudevan Vasudevan Venkatraman A.Ramkumar T.Muthukumar A.Sheela M.Vetrivel R.J.Vijaya Saraswathi F.T.Josh Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Computer Science Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Electrical and Electronics Engineering Kongunadu College of Engineering and TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Kongu Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Narasu’s Sarathy Institute of TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Panimalar Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Karunya Institute of Technology&SciencesTamil NaduIndia

MigroGrid(MG)has emerged to resolve the growing demand for *** because of its inconsistent output,it can result in various power quality(PQ)*** is a problem that is becoming more and more important for the reliability of power systems that use renewable energy ***,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply ***,this research focuses on power quality enhancement in the MG using hybrid shunt ***,the performance of the filter mainly depends upon the design,and stability of the *** efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)*** performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this ***,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in *** the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

关键词： Artificial intelligence resistive inductive load shunt hybrid filter smart grid adaptive fuzzy back-stepping power factor

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Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

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Australian Journal of electrical and electronics engineering 2025年第2期22卷 271-282页

作者： Karambir Yadav, Ashwani Kumar Madan, Charanjeet Department of Electrical Engineering ASET Amity University Jaipur India Department of Electronics & Communication Engineering ASET Amity University Jaipur India Department of Electrical Engineering Guru Jambheshwar University of Science & Technology Hisar India

PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm. ©, Engineers Australia.

关键词： Optimization

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An adaptive fractional controller design for automatic voltage regulator system: sigmoid-based fractional-order PID controller

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Neural Computing and Applications 2024年第23期36卷 14409-14431页

作者： Sahin, Ali Kivanc Cavdar, Bora Ayas, Mustafa Sinasi Department of Electrical and Electronics Engineering Erzurum Technical University Erzurum25050 Turkey Department of Energy Systems Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Electrical and Electronics Engineering Karadeniz Technical University Trabzon61080 Turkey

The primary objective of a power system is to provide safe and reliable electrical energy to consumers. This objective is achieved by maintaining the stability of the power system, a multifaceted concept that can be divided into three distinct classes. The focus of this study is on one of these classes, voltage stability. A critical component in maintaining voltage stability is the automatic voltage regulator (AVR) system of synchronous generators. In this paper, a novel control method, the sigmoid-based fractional-order PID (SFOPID), is introduced with the aim of improving the dynamic response and the robustness of the AVR system. The dandelion optimizer (DO), a successful optimization algorithm, is used to optimize the parameters of the proposed SFOPID control strategy. The optimization process for the DO-SFOPID control strategy includes a variety of objective functions, including error-based metrics such as integral of absolute error, integral of squared error, integral of time absolute error, and integral of time squared error, in addition to the user-defined Zwee Lee Gaing’s metric. The effectiveness of the DO-SFOPID control technique on the AVR system has been rigorously investigated through a series of tests and analyses, including aspects such as time domain, robustness, frequency domain, and evaluation of nonlinearity effects. The simulation results are compared between the proposed DO-SFOPID control technique and the fractional-order PID (FOPID) and sigmoid-based PID (SPID) control techniques, both of which have been tuned using different metaheuristic algorithms that have gained significant recognition in recent years. As a result of these comparative analyses, the superiority of the DO-SFOPID control technique is confirmed as it shows an improved performance with respect to the other control techniques. Furthermore, the performance of the proposed DO-SFOPID control technique is validated within an experimental setup for the AVR system. The simulation res

关键词： Three term control systems

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Optimally distributed generator placement and phasor measurement unit integration for enhanced distribution system performance: a particle swarm optimisation-based approach

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International Journal of Energy Technology and Policy 2025年第1-2期20卷 182-207页

作者： Mol, N. Remya Bindumol, E.K. Achary, Rajesh Kuttappan Mini, V. Department of Electrical and Electronics Government Engineering College Kerala Thrissur680009 India APJ Abdul Kalam Technological University India Department of Electrical and Electronics Government Engineering College Kerala Palakkad678633 India

An optimally sized and placed distributed generator (DG) plays a vital role in the distribution sector to provide voltage stability by injecting sufficient active power to the access point. The phasor measurement unit (PMU) has a major role in the microgrid to monitor and control the grid effectively. It assesses the voltage magnitude and phase angle at its location along with the current magnitude and phase angle of the adjacent branch. This paper focuses on the particle swarm optimisation (PSO) algorithm to determine the optimal position and size of DG and binary-based PSO (BPSO) to identify optimal locations for PMUs to guarantee full observability of the network. The proposed method was tested on an IEEE 33-bus radial system. Results showed that power loss is reduced by 48.7% and 57.6% with single and two DGs, respectively, with penetration levels of 67.74% and 43.97%. The number of PMUs reduced with a cost reduction of 7%. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Phasor measurement units

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Impact of Different Integration Times on Distributions of Rain Rates for Predictions of Rain Attenuation

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Progress in Electromagnetics Research C 2025年 154卷 229-238页

作者： Hassan, Mohammad Rofiqul Rafiqul, Islam Md. Habaebi, Mohamed Hadi Suriza, Ahmad Zabidi Badron, Khairayu Budalal, Asma Ali Mahfuz, Md. Mahmudul Hasan Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia College of Electrical and Electronics Technology Benghazi Libya Department of Electrical and Computer Engineering Concordia University Canada

All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction method of rain attenuation is established and used globally based on local rain intensity measurement. Required rain intensity used for attenuation prediction is generally measured at a point with 1-min integration time or converted from higher integration time to 1-min. Recent measurements of rain intensity with a 10-second integration time indicate that intensity is not uniform over a 1-minute duration. Consequently, the statistics of rain intensity distribution and attenuation predictions are influenced by measurements with integration times shorter than 1 minute. It has been established that an integration time of 0.01% provides the optimal fit for actual rain rate data. This paper presents the rain intensity distributions from data measured with 2-min, 1-min, 30-sec, 20-sec, and 10-sec integration times, and it has impact on rain rate distributions as well as rain attenuation predictions. © 2025, Electromagnetics Academy. All rights reserved.

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Detecting and classifying breast masses via YOLO-based deep learning

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Neural Computing and Applications 2025年第17期37卷 11555-11582页

作者： Karaca Aydemir, Büşra Kübra Telatar, Ziya Güney, Selda Dengiz, Berna Department of Biomedical Engineering Baskent University Ankara Turkey Department of Electrical and Electronics Engineering Baskent University Ankara Turkey Department of Industrial Engineering Baskent University Ankara Turkey

Breast cancer has a high incidence and mortality rate in the female population. Mammography is the most reliable method for early and accurate diagnosis of breast cancer. Automated detection and classification of breast masses on mammograms is a challenging task and is essential to assist radiologists in accurately diagnosing breast masses. The aim of this study is to develop a Computer-Aided Diagnosis (CAD) system based on You Look Only Once (YOLO) for identifying breast masses and classifying them as benign or malignant. We propose a YOLOv5-CAD framework that uses a transfer learning approach. Two datasets, CBIS-DDSM and VinDr-Mammo, are utilized for training from scratch. The model weights and parameters are subsequently transferred and fine-tuned onto the smaller INBreast dataset. Furthermore, an analysis is conducted to assess the impact of various data augmentation techniques during the training phase on enhancing model performance. The proposed framework demonstrates encouraging fivefold cross-validation evaluation results. To conclude, transfer learning from CBIS-DDSM achieves 0.843 mAP, precision of 0.855, recall of 0.774, while transfer learning from VinDr- Mammo reaches 0.84 mAP, precision of 0.829, recall of 0.787. Furthermore, the performance of the two fine-tuned models was tested on both the MIAS dataset and the private dataset from Başkent University Ankara Hospital. Such promising performance could be useful for the CAD frameworks being developed to support radiologists as a second opinion reader for the detection and classification of breast masses. © The Author(s) 2025.

关键词： Transfer learning

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Design and Implementation of Integrated Microgrid Based Predictive Control Scheme 5

Design and Implementation of Integrated Microgrid Based Pred...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Lenine, D. Kumar, P. Sai Sampath Kumari, J. Surya Raja, D. Department of Electrical and Electronics Engineering Nandyal India Department of Electrical and Electronics Engineering Puducherry India

ISBN: (纸本)9798350376685

This paper introduces a novel approach called Fuzzy-MPPDC designed specifically for Renewable Energy Systems (RES). It mainly aims to address voltage fluctuations arising from variable power demands and the unpredictable outputs of RES. Fuzzy logic enhances adaptability to grid requirements and BES conditions, facilitating power exchange with the utility grid. Unlike traditional methods, it employs a flexible objective function dynamically adjusting to external factors. MATLAB/Simulink simulations validate Fuzzy-MPPDC, showing precise reference tracking and minimal Total Harmonic Distortion (THD) across various loads. This marks a significant advancement in RES control, promising improved stability and performance in voltage regulation. © 2024 IEEE.

关键词： Harmonic distortion

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