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Tuning function based adaptive prescribed-time parameter estimation and tracking control design

Automatica 2025年 177卷

作者： Shi, Wenrui Keliris, Christodoulos Hou, Mingzhe Polycarpou, Marios M. Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin150001 China KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

Most existing prescribed-time (PT) control results focus on the stabilization/regulation problem, with only a few results considering the tracking control problem. In this paper, an adaptive PT tracking control method is proposed for a class of strict feedback nonlinear systems with linearly parametric uncertainties which can be non-vanishing and include both matched and mismatched parts. By using the normalization technique and a novel cutoff function, a two-filter-based parameter estimation error reconstruction mechanism is designed. Furthermore, an improved tuning function based backstepping design solution is provided. Different from the classic tuning function based backstepping results, some additional terms which are designed through the reconstructed parameter estimation errors, are embedded into the virtual control laws, the actual control law and the adaptive laws. Moreover, in order to avoid potential numerical problems in the implementation of the scheme, a modified adaptive PT control scheme is proposed. Finally, two simulation examples are employed to illustrate the effectiveness of the proposed control method. © 2025 Elsevier Ltd

关键词： Adaptive control systems

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MUFFIN-HGCN: Multi-feature Fusion Hierarchical-GCN for 3D Object Detection

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chang Jiang, Aimin Zhou, Jia Li, Min Zhu, Yanping Kwan, Hon Keung Hohai University College of Information Science and Engineering Changzhou213200 China Changzhou University School of Microelectronics and Control Engineering Changzhou213164 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

3D object detection play a pivotal role in various applications, such as autonomous driving and environmental perception. However, the challenging task of detecting targets (e.g., vehicles and pedestrians) in 3D point clouds obtained from LiDAR sensors is hindered by two primary factors. Firstly, point clouds are non-Euclidean data and lack rigid data properties. Secondly, these point clouds are sparse and deficient in semantic information. In this paper, we propose a novel deep learning framework for 3D object detection. The proposed approach involves two stages. Specifically, we first present an innovative fusion technique that combines images and point clouds using transformers. This technique enhances the integration of semantic and geometric features while accurately mapping points to pixels without relying on camera extrinsic parameters. Additionally, a multi-level graph convolutional network (GCN) architecture is further introduced for object detection. This architecture effectively handles the non-Euclidean characteristics of point clouds while achieving precise object identification within them. A comprehensive series of experiments on the KITTl and nuScenes datasets validate the effectiveness of the proposed framework. © 1967-2012 IEEE.

关键词： Object detection

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Multi-Agent Imitation Learning Based Energy Management of a Microgrid With Hybrid Energy Storage and Real-Time Pricing

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IEEE Internet of Things Journal 2025年

作者： Gao, Shuhua Xu, Yizhuo Zhang, Zhaoqian Wang, Zhengfang Zhou, Xiaoyu Wang, Jing Shandong University School of Control Science and Engineering Jinan250100 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore Second Construction Limited Company of China Construction Eighth Engineering Division Jinan Shandong China

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver;then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method's proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy's multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids. © 2014 IEEE.

关键词： Costs

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Improving Software Reliability with Rust: Implementation for Enhanced control Flow Checking Methods

Improving Software Reliability with Rust: Implementation for...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jacopo Sini Mohammadreza Amel Solouki Massimo Violante Giorgio Di Natale Department of Control and Computer Engineering Politecnico di Torino Turin Italy Univ. Grenoble Alpes CNRS Grenoble INP1 TIMA Grenoble France

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The C language, traditionally used in developing safety-critical systems, often faces memory management issues, leading to potential vulnerabilities. Rust emerges as a safer and secure alternative, aiming to mitigate these risks with its robust memory protection features, making it suitable for producing reliable code in critical environments, such as the automotive industry. This study proposes employing Rust code hardened by control Flow Checking (CFC) in real-time embedded systems, which software is traditionally developed by Assembly and C languages. The methods have been implemented at the application level, i.e., in the Rust source code, to make them platform-agnostic. A methodology for leveraging the Rust advantages is presented, such as stronger security guarantees and modern features, to implement these methods more effectively. Highlighting a use case in the automotive sector, our research demonstrates the Rust capacity to enhance system reliability through CFC, especially against Random Hardware Faults. Two CFC algorithms from the literature, YACCA, and RACFED, have been implemented in the Rust language to assess their effectiveness, obtaining 46.5% Diagnostic Coverage for the YACCA method and 50.1% for RACFED. The proposed approach is aligned with functional safety standards, showcasing how Rust can balance safety requirements and cost considerations in industries reliant on software solutions for critical functionalities.

关键词： Industries Costs Codes Systematics Source coding C languages Software reliability Protection Standards Automotive engineering

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Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhanced Performance

Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhan...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Departement of Computer Engineering College of Computer Qassim University Buraidh Saudi Ababia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

High node mobility, rapid topology changes provide specific challenges for vehicular ad hoc networks (VANETs), which have an immediate impact on the routing protocols' performance. Traditional approaches, like the Optimized Link State Routing (OLSR) protocol, provide proactive route management but fail to fully account for critical dynamic parameters like link stability, relative vehicle speed, node distance, and bandwidth availability. This work proposes a hybrid method combining OLSR with Q-learning to facilitate real-time adaptive routing. The model leverages dynamic metrics to proactively evaluate links while employing Q-learning to optimize routing decisions based on rewards computed from performance factors like delay, packet loss rate, and link duration. Simulation results demonstrate that our approach significantly outperforms classic OLSR. The improvements include reduced packet loss rates, increased average throughput, lower average latency, and a reduction in control overhead. These findings confirm that integrating dynamic metrics and adaptive learning effectively addresses the challenges posed by VANETs.

关键词： Q-learning Simulation Vehicular ad hoc networks Packet loss Routing Throughput Routing protocols Stability analysis Topology Vehicle dynamics

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Investigating Reinforcement Learning for Histopathological Image Analysis

Investigating Reinforcement Learning for Histopathological I...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Mohamad, Mohamad Ponzio, Francesco Gassier, Maxime Pote, Nicolas Ambrosetti, Damien Descombes, Xavier Université Côte d’Azur INRIA CNRS I3S INSERM IBV Sophia Antipolis France Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Pathology Bichat Hospital Assistance Publique-Ĥopitaux de Paris Paris France Department of Pathology CHU Nice Université Côte d’Azur Nice France

In computational pathology, whole slide images represent the primary data source for AI-driven diagnostic algorithms. However, due to their high resolution and large size, these images undergo a patching phase. In this paper, we approach the diagnostic process from a pathologist’s perspective, modeling it as a Sequential decision-making problem using reinforcement learning. We build a foundational environment designed to support a range of whole slide applications. We showcase its capability by using it to construct a toy goal conditioned Navigation environment. Finally, we present an agent trained within this environment and provide results that emphasize both the promise of reinforcement learning in histopathology and the distinct challenges it faces. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Deep reinforcement learning

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Observer-based finite-time control of conformable Takagi–Sugeno fuzzy systems with time delay

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Asian Journal of control 2025年

作者： Kahouli, Omar Gassara, Hamdi Aloui, Ali Elleuch, Zied Bouteraa, Yassine Department of Electronics Engineering Community College University of Ha'il Ha'il Saudi Arabia Laboratory of Science and Technology of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Department of Computer Science Community College University of Ha'il Ha'il Saudi Arabia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

This paper examines finite-time (F-T) observer-based control of fractional-order nonlinear systems with time delay, employing the Takagi–Sugeno fuzzy (T-SF) approach. This study utilizes the conformable fractional derivative (CFD) as its foundational framework. Sufficient conditions are formulated to guarantee that the augmented vector, consisting of the state estimates and the state estimation errors, stays within a ball centered at the origin. This applies not only to specified initial conditions but also to predefined disturbances. These conditions are then reformulated as linear matrix inequalities (LMIs) conditions, which can be efficiently solved using the LMI toolbox. An illustrative example is given to illustrate the proposed result. © 2025 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Delay-sensitive applications

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Convergence of energy-based learning in linear resistive networks

arXiv

引用

arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis

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Biomedical Signal Processing and control 2025年 109卷

作者： ZainEldin, Hanaa Bamaqa, Amna Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa A. Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura 35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah 42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu 46421 Saudi Arabia

One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, current models fail to combine innovative object identification and classification methods, limiting their diagnostic performance. A bibliometric study of 1145 research articles identified key gaps, including the lack of integration between advanced object detection models like YOLOv9 and YOLOv10 with high-performance classification architectures such as EfficientNet B7, as well as the limited utilization of diverse imaging modalities. This study proposes a novel ensemble model for Breast Cancer Classification and Detection by combining YOLOv9, YOLOv10, and EfficientNet B7 (BC²D-Y/EN) to address these gaps. The proposed model combines the advanced object detection capabilities of YOLOv9 and YOLOv10 with the high-performance classification power of EfficientNet B7. This method aims to improve detection accuracy and computational efficiency in breast cancer identification. The proposed approach is validated using four publicly available datasets: CBIS-DDSM, CBIS-DDSM RGB breast cancer dataset, The Breast1 MRI dataset, and the Breast Ultrasound Tumor Object Detection dataset, all containing expert-annotated cases of benign and malignant tumors. The experimental findings indicate that the suggested model surpasses other models, achieving precision rates of 98.56% with EfficientNet B7, 98.3% with YOLOv9, and 100% with YOLOv9. These findings highlight the advantages of advanced models and diverse imaging methodologies to enhance diagnostic accuracy and effectiveness in breast cancer detection. © 2025 Elsevier Ltd

关键词： Breast cancer detection (BCD) Classification Deep learning (DL) EfficientNet B7 YOLOv10 YOLOv9

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Geometric Reachability for Attitude control Systems via Contraction Theory

arXiv

引用

arXiv 2025年

作者： Xu, Chencheng Jafarpour, Saber Zhao, Chengcheng Shi, Zhiguo Chen, Jiming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States College of Control Science and Engineering Zhejiang University Hangzhou China

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold SO(3) × 3 and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the Riemannian distance between nearby trajectories of the attitude control systems. By combining these trajectory bounds with numerical simulations, we provide a simulation-based algorithm to over-approximate the reachable sets of attitude systems. We show that the search for optimal metrics for distance bounds can be efficiently performed using semidefinite programming. Additionally, we introduce a practical and effective representation of these over-approximations on manifolds, enabling their integration with existing Euclidean tools and software. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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