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STGNN: a new self-trained generalized neural networks task space control for robot manipulators

Neural Computing and Applications 2025年 1-26页

作者： Elmogy, Ahmed Elawady, Wael El-Ghaish, Hany Computer Engineering Department Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta31527 Egypt

The inherent uncertainties in the dynamic and kinematic parameters of robot manipulators pose significant challenges for their control in task space. This paper introduces an innovative adaptive sliding mode control strategy, leveraging self-trained generalized neural networks to enhance the precision and robustness of robot manipulator control. The proposed framework called STGNN integrates deep neural networks for the real-time estimation and adjustment of dynamics’ and kinematics’ parameters. Key contributions of this work include the development of semi-supervised learning models for adaptive dynamics and kinematics estimation. By training the generalized neural network on different trajectories, the same models can be utilized to operate on a variety of tasks, significantly improving the robustness, scalability, and adaptability of the control system. The STGNN approach allows the models to leverage both labeled and unlabeled data, incorporating a self-training mechanism that enhances their ability to generalize and adapt to diverse operational scenarios. This capability is particularly beneficial for real-time applications, where the ability to learn and adapt on the fly is crucial. Simulation results demonstrate the superior performance of the STGNN framework in controlling robot manipulators in operational space, showcasing its potential for real-world applications. The proposed method significantly advances the adaptability, accuracy, and efficiency of robot control systems, offering notable improvements over conventional approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self adjusting control systems

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Path Planning for Multi-agent Systems Using Deep Q-Networks Reinforcement Learning 25th

Path Planning for Multi-agent Systems Using Deep Q-Networks...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Alispahić, Ibrahim Tahirović, Adnan Faculty of Electrical Engineering Department of Automatic Control and Electronics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9783031720611

This work presents a comprehensive study of the application of multi-agent reinforcement learning (MARL) based on deep Q-networks (DQN), aiming to enhance the cooperation and coordination of multiple agents in complex environments. The core problem addressed is the multi-agent traveling salesman problem, i.e. the effective collaboration and target-reaching by multiple agents. This challenge is inherently present in a variety of applications, including inspection and maintenance tasks based on autonomous aerial robotic systems. The research delves into the intricacies of multi-agent systems, emphasizing the dynamic interaction between agents and the environment. Although the proposed algorithm is developed and trained in a planar space, its principles and methodologies are easily extendable to any operational environment. The proposed approach is capable of training on a number of different scenarios, using various numbers of agents and targets, regardless of their initial positions, making it highly adaptable to scenarios like aerial inspections, where the number of agents, the number of inspection sites and their locations are unknown a priori. Furthermore, the work outlines how the enhanced DQN algorithm can be tailored for scenarios with numerous autonomous agents and targets, ensuring efficient path planning and target acquisition in constrained and unstructured spaces. This work also provides a solid foundation for future research in integrating advanced reinforcement learning techniques into the design and operational strategies for solving tasks, which can be described by the multi-agent traveling salesman problem, including inspection and maintenance with an autonomous multi-agent system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traveling salesman problem

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration 5

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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5th National and the 1st International Conference on Applied Research in Electrical engineering, AREE 2025

作者： Mousavi, Mohammad Hossein Moradi, Hassan Rouzbehi, Kumars Razi University Department of Computer and Electrical Engineering Kermanshah Iran University of Seville Department of System Engineering and Automatic Control Seville Spain

ISBN: (纸本)9798350377170

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment. © 2025 IEEE.

关键词： Wind power integration

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Aerial Robots Carrying Flexible Cables: Dynamic Shape Optimal control via Spectral Method Model

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IEEE Transactions on Robotics 2025年 41卷 3162-3182页

作者： Shen, Yaolei Franchi, Antonio Gabellieri, Chiara Faculty AE Enschede7500 Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy

In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations combined with boundary conditions described by ordinary differential equations. The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a finite low-dimensional space spanned by orthogonal basis functions. Based on such a reduced-order model, nonlinear model predictive control is implemented online to realize both position and shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed POD-based reduced modeling and optimal control paradigms are verified in simulation using an accurate high-dimensional finite difference method-based model and experimentally using a real quadrotor and a cable. The results show the viability of the POD-based predictive control approach (allowing to close the control loop on the full system state) and its superior performance compared to an optimally tuned proportional–integral–derivative (PID) controller (allowing to close the control loop on the quadrotor state only). © 2004-2012 IEEE.

关键词： Orthogonal functions

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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A Framework for Real-Time Monitoring of Power Consumption of Distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

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15th International Conference on Cloud Computing and Services Science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the context information about the performed calculation. The first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. The second part is dedicated to the testing of distributed data storage, called Scalable Distributed Two-Layered Data Structure (SD2DS). The results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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Voltage and power control for distributed generation units in islanded microgrids 6th

Voltage and power control for distributed generation units i...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Rouch, T. Bagheri M’zoughi, F. Aboutalebi, P. Ahmad, I. Garrido, A.J. Garrido, I. Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country Bilbao Spain

ISBN: (纸本)9781032905570

This article presents an optimized control strategy tailored for DC islanded microgrids, featuring a voltage controller based on mixed H2/H∞ state feedback using linear matrix inequalities (LMIs) and a power controller guided by droop characteristics. A polytopic modeling approach addresses system uncertainties and nonlinearities. The controller aims to stabilize the system, reject disturbances, equitably distribute power among distributed generation units (DGUs), and minimize energy consumption. Regional pole placement ensures welldamped transient responses. Simulation results demonstrate effective management of load perturbations and DGU disconnections, with a comparative analysis against an H∞ controller offering additional insights into performance. © 2025 The Author(s).

关键词： Microgrids

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Hybrid offshore wind turbines: control-oriented modeling approaches 6th

Hybrid offshore wind turbines: Control-oriented modeling app...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. This study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 The Author(s).

关键词： Offshore wind turbines

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UWB System for Frequent Positioning of Moving Objects 21st

UWB System for Frequent Positioning of Moving Objects

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21st European, Mediterranean, and Middle Eastern Conference on Information Systems, EMCIS 2024

作者： Paszek, Krzysztof Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783031813214

Object positioning is an essential issue in the context of autonomous vehicles and AGVs in Industry 4.0 and smart cities. Based on the position, it is possible not only to track objects, but with appropriate positions’ accuracy and frequency, it is possible to prevent dangerous situations. The information about the possibility of a dangerous situation must be available in advance so that the superior system, driver, or operator can take the appropriate action. A UWB is a promising technology for object positioning, even though UWB systems are used for positioning objects in a limited area, mostly indoors. Nothing stands in the way of creating many small areas in high-risk areas, e.g. intersections and manoeuvring yards. This ultra-wideband technology allows to objects positioning with high accuracy of a few centimeters. However, the accuracy of this system depends on the speed of movement of the positioned objects and the ranging frequency to individual stationary reference nodes – anchors. Therefore, it is important to exchange messages in the network of UWB nodes during ranging runs as quickly as possible. This article analyses the modified UWB positioning system, which maximizes the frequency of acquiring the distance between nodes and, thus the frequency of determining the object’s positions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ultra-wideband (UWB)

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