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Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control

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Science China(Information Sciences) 2023年第5期66卷 182-195页

作者： Xiaofang HU Leimin WANG Chuan-Ke ZHANG Xiongbo WAN Yong HE School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.

关键词： spatiotemporal neural networks discontinuous activations time-varying coefficients fixed-time stabilization aperiodically switching control

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Dual Star Induction Machine Driven by Direct Torque Control Using Sliding Mode Speed Controller 6

Dual Star Induction Machine Driven by Direct Torque Control ...

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6th IEEE International Conference on advanced systems and Emergent Technologies, IC_ASET 2023

作者： Latrech, Fatma Zohra Rhouma, Asma Ben Khedher, Adel Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798350321029

This paper presents the direct torque control (DTC) of a dual star induction machine (DSIM) employing a sliding mode speed controller. The comparison process is achieved by comparing the efficiency of two types of speed controllers: the proportional integral (PI) controller and the sliding mode (SM) controller. Simulation tests were conducted in the MATLAB/Simulink environment and the obtained results demonstrate the superiority of the SM speed controller in terms of robustness to torque and speed variations, as well as the rapidity of the DSIM operation. © 2023 IEEE.

关键词： Stars

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FEM and New Diffusive Representation Technique Application for DSIM Thermal Transition Analysis 6

FEM and New Diffusive Representation Technique Application f...

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6th IEEE International Conference on advanced systems and Emergent Technologies, IC_ASET 2023

作者： Guenenna, Thouraya Smida, Khaled Ben Khedher, Adel Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798350321029

Induction Motor (IM) Lifetime is very sensitive to temperature increase due to the stator windings fragility and motor losses. Therefore, thermal analysis is a crucial process that should be taken into consideration while simulating the mathematical model of the IM. This paper presents a modeling study of DSIM (Double Stars Induction Machine) with one broken phase stator. Additionally, this article proposes two thermal methods that are used to predict and compared the temperature rise in DSIM shifted by 30°: the finite element method (FEM) and the diffusive representation approach. Thus, the main objective of this article is to show the impact of different machine powering scenarios (rated-load, no-load and rotor-locked) on the transient temperature variation of the DSIM. The effectiveness of the diffusive approach and its ability to monitor the temperature in the machine has been proved by comparative study of two temperature prediction models. © 2023 IEEE.

关键词： Finite element method

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Implementation of MPPT Algorithms to a Developed Photovoltaic Emulator: Study and Comparative Analysis 11

Implementation of MPPT Algorithms to a Developed Photovoltai...

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11th IEEE International Conference on systems and Control, ICSC 2023

作者： Mahmoud, Zouhaira Ben Moussa, Intissar Khedher, Adel Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798350304886

Photovoltaic (PV) emulators (PVEs) become a critical tool to test the PV generation system such as the maximum power point tracking (MPPT) converter and the PV system control. The latter ensures a controllable light source environment that mimics the output characteristics of a real PV panel. In this context, this paper deals with the implementation of different MPPT techniques to a developed low-cost PV emulator based on two-diode model. Mainly, The PVE structure is formed by three parts that are the PV model, the control strategy, and the power stage. In an initial phase, the PV emulator system is presented, and its three main components are described. In a second phase, three MPPT algorithms, namely, the perturb and observe (P&O) technique, the incremental conductance (IC) technique and the sliding mode control (SMC) technique are studied and applied to the developed PVE. The acquired simulation results show that the developed PVE can follow accurately the output characteristics of the PV module. Moreover, the studied MPPT algorithms allows the PVE to operate at its maximum power during irradiance and temperature variations. As compared to the P&O and the IC techniques, the SMC provides a good response in MPP tracking without considerable ripples. © 2023 IEEE.

关键词： Sliding mode control

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Performance Investigation of Three-Stage Modular Smart Transformer with Sliding Mode Control 6

Performance Investigation of Three-Stage Modular Smart Trans...

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6th IEEE International Conference on advanced systems and Emergent Technologies, IC_ASET 2023

作者： Helali, Hiba Khedher, Adel Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798350321029

The Smart Transformer (ST) is an intelligent power transmission device between AC and DC grids, which facilitates the integration of renewable energy resources, electric vehicle charging stations, and energy storage batteries into the distribution grid. This paper proposes a three-stage smart transformer model comprising a Cascaded H-Bridge (CHB) rectifier for the input stage, three dual active bridge converters for the isolation stage, and a four-leg inverter for the output stage. In order to improve the dynamic performance of this model, the sliding mode control has been applied to its input and output stages, and the soft switching control has been used in the isolation stage. To investigate the performance of the ST control strategy, a series of simulations were performed under disturbances, namely, voltage drop, voltage swell, harmonics distortion, and flickers in the MATLAB environment. The obtained results confirm the robustness of the ST controller against these disturbances. © 2023 IEEE.

关键词： Electric inverters

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YOLO-Based Detector for Enhancing Workplace Safety: Real-Time PPE Detection and Monitoring 21

YOLO-Based Detector for Enhancing Workplace Safety: Real-Tim...

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2024 IEEE/ACS 21st International Conference on Computer systems and Applications, AICCSA 2024

作者： Amri, Souha Ouannes, Laila Sahbani, Anis Ben Amara, Najoua Essoukri Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Enova Robotics S.A. Paris France

ISBN: (纸本)9798331518240

Ensuring the safety of workers in hazardous environments is crucial, given the potential severity of accidents. Indeed, Personal Protective Equipment (PPE), including safety helmets, vests, and boots, is vital in mitigating risks. However, adherence to PPE usage often falls short due to inadequate safety awareness among workers. To address this challenge, we aim to develop a robust safety monitoring system by leveraging advancements in deep learning and object detection technology. Our proposed method uses YOLOv7 and YOLOv8 algorithms for real-time detection and classification of safety equipment, including helmets, vests, and boots. For this aim, we carefully collect and annotate datasets from diverse sources like Roboflow and Kaggle. Then, we accurately evaluate performance metrics such as precision, recall, and speed through rigorous training and testing of various YOLOv7 and YOLOv8 configurations on annotated data batches. By conducting thorough comparative analyses, we aim to identify the most effective model for each PPE category, considering both detection accuracy and computational efficiency. Finally, the object detection system is deployed through a web interface to determine whether a person is protected or not. © 2024 IEEE.

关键词： Deep learning

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An EMC Study for Renewable Energy Applications Using AI Algorithms

An EMC Study for Renewable Energy Applications Using AI Algo...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Zitouna, Bessem Tlig, Mohamed Hedia, Sassia Hadj Slama, Jaleleddine Ben Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798350325539

In this paper, it has been established that hybrid artificial intelligence (AI) is an efficient tool in performing electromagnetic compatibility (EMC) studies for power electronics systems in renewable energy applications. Indeed, a three-dimensional electromagnetic (EM) inverse method is presented for modeling and characterizing radiating sources using the near-field technique. The proposed method is based on time-domain (TD) analysis. AI-based techniques allow performing sources identification and classification with a guaranteed convergence and an adequate computing time. Indeed, the TD EM inverse method is coupled with a hybrid AI algorithm based on the implementation of a particle swarm optimization (PSO) algorithm along with a Pseudo Zernike Moment Invariant (PZMI) descriptor and an artificial neural network (ANN). The proposed method is applied to a simulation of a structure with a transient radiation behavior, such as those used in renewable energy systems. Configuration parameters are fully mastered to avoid cumulative errors related to experimental measurements. A comparative study has been carried out between reference and estimated EM field distributions using TD inverse method based on the hybrid AI method on the one hand and on genetic algorithms (GA) on the other. Obtained results prove the efficiency of the proposed algorithm, in particular in terms of convergence speed and accuracy. © 2023 IEEE.

关键词： Time domain analysis

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State of Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Networks in Electric Vehicle Applications

State of Charge Estimation of Lithium-Ion Batteries Using Co...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Gaich, Marwa Jemmali, Sabeur Manai, Bilal National School of Engineers of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Cegep de l'Outaouais Department of Electrical Engineering GatineauQC Canada

ISBN: (纸本)9798350389838

The emergence of Electric Vehicles (EVs) has introduced new issues and complexities, particularly in accurately gauging the remaining battery capacity or the distance an EV can travel before needing a recharge, known as the State of Charge (SoC) estimation problem. This study aims to develop an innovative solution to this problem while considering the usual computational complexity requirements. It emphasizes maintaining low memory and computational demands for practical implementation within a Battery Management System (BMS). By leveraging contemporary tools from artificial intelligence, especially Machine Learning (ML) models, the findings indicate that accurate SoC prediction can be achieved with simple ML models, given a well-curated dataset. Specifically, a Convolutional Neural Network (CNN) with two hidden layers and a limited number of hidden neurons shows promising results in estimating the SoC for a lithium-ion battery cell while maintaining low computational complexity. At 25°C, the CNN model reports a mean absolute error (MAE) of 0.97 and a root mean squared error (RMSE) of 1.71. This research offers significant potential for future advancements in SoC estimation, providing a solid foundation for addressing the issue and suggesting an optimal ML architecture for future BMSs to improve the SoC prediction accuracy. © 2024 IEEE.

关键词： State of charge

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Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system

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Concurrent Engineering Research and Applications 2024年第1-4期32卷 3-19页

作者： Naceur, Ferdaws Ben Toumi, Sana Ben Salah, Chokri Mahjoub, Mohamed Ali Tlija, Mehdi National Engineering School of Sousse LATIS - Laboratory of Advanced Technology and Intelligent Systems University of Sousse Sousse Tunisia University of Brest Brest France University of Monastir Monastir Tunisia ISSAT of Sousse University of Sousse Sousse Tunisia Department of Industrial Engineering College of Engineering King Saud University Riyadh Saudi Arabia

This paper describes a decentralised smart grid system containing renewable energies, storage systems and distributed generation with human control and intervention. The importance of each element and the interaction between them leads to think about a decision-making strategy. In fact, the integration of a Photovoltaic Panel (PVP) is used due to its availability and its participation in the carbon emissions reduction. Also, a battery is required to fill a power gap or absorb extra generated energy. Moreover, an optimal sizing is needed to get an efficient system with minimum cost. Also, an energy management strategy (EMS) is essential to ensure the power resources scheduling in order to keep a continuous equilibrium supply-demand of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. In the first part, the proposed smart grid optimal sizing is determined under real weather data (solar radiation) of the city of Sousse, Tunisia, using the Hybrid Optimization of Multiple Energy Resources (HOMER) software technique. This approach is chosen thanks to its simplicity, effectiveness, and high precision compared to traditional techniques. In this paper, several configurations (Grid, (Grid-battery), (Grid-PVP), (Grid-PVP-battery)) are studied. The obtained results prove that the (Grid-PVP-battery) system configuration is the most efficient and economical solution. In the second part, a robust energy management strategy (EMS) is proposed for two smart grid configurations (grid-battery, grid-PVP-battery). This strategy is based on Fuzzy Logic Control (FLC) thanks to its non-linear modelling and its ability to make decisions relating to energy management. The primary goal of the suggested (EMS) is to ensure the energy resources scheduling in order to keep a continuous equilibrium among the production and consumption of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. As input data, (F

关键词： MATLAB

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Active learning attraction basins of dynamical system

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Chinese Physics B 2025年第5期34卷 115-122页

作者： Xiao-Wei Cao Xiao-Lei Ru Gang Yan MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physical Science and EngineeringTongji UniversityShanghai 200092China National Key Laboratory of Autonomous Intelligent Unmanned Systems MOE Frontiers Science Center for Intelligent Autonomous Systemsand Shanghai Research Institute of Intelligence Science and TechnologyTongji UniversityShanghai 200092China

Dynamical systems often exhibit multiple attractors representing significantly different functioning conditions.A global map of attraction basins can offer valuable guidance for stabilizing or transitioning system *** a map can be constructed without prior system knowledge by identifying attractors across a sufficient number of points in the state ***,determining the attractor for each initial state can be a laborious ***,we tackle the challenge of reconstructing attraction basins using as few initial points as *** each iteration of our approach,informative points are selected through random seeding and are driven along the current classification boundary,promoting the eventual selection of points that are both diverse and *** results across various experimental dynamical systems demonstrate that our approach requires fewer points than baseline methods while achieving comparable mapping ***,the reconstructed map allows us to accurately estimate the minimum escape distance required to transition the system state to a target basin.

关键词： complex system attraction basin active learning

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