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Prescribed Performance control for a Nonlinear System With Actuator Saturation via Anti-Windup Design

IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Lai, Wenxin Li, Yuanlong Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai 200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering Charlottesville 22904 VA United States

In this paper, within the prescribed performance control framework, we revisit the output tracking problem of a nonlinear system with input saturation, user-specified performance and external disturbances. To handle the intrinsic conflict between actuator saturation and performance requirements, we introduce a novel auxiliary system, which is driven by an artificial saturation nonlinearity, to generate modification signals for relaxing the prescribed performance functions. To further alleviate the adverse effects caused by actuator saturation, we incorporate a new performance-based anti-windup compensator into the control framework. In particular, the auxiliary system is designed to adjust the external performance requirements, and the anti-windup compensator is used for improving the internal saturation handling capability. Moreover, we employ a predetermined weighting coefficient to balance between the auxiliary system and the anti-windup compensator for better control performance. To deal with external disturbances, we construct a series of disturbance observers. With the help of dynamic surface control strategies, we develop a state-feedback control law. Theoretical analysis establishes the boundedness of all signals. Finally, an academic example and a tunnel diode circuit system are provided to demonstrate the effectiveness of our control strategy. © 2004-2012 IEEE.

关键词： activation mechanism Actuator saturation anti-windup disturbance observers prescribed performance reference tracking

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KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning

arXiv

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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation

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Mapping cultural heritage sites at risk: A support tool for heritage sites management

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Journal of Urban Management 2025年

作者： Brandano, Maria Giovanna Conti, Chiara Modica, Marco Urso, Giulia Gran Sasso Science Institute – Social Sciences Viale Luigi Rendina 26-28 L'Aquila 67100 Italy Department of Computer Control and Management Engineering “Antonio Ruberti” of the University La Sapienza Italy

Under the threat of climate change, the world has become increasingly unsafe, with extreme weather events causing devastation and high economic costs. These impacts are heterogeneous because of the interaction between different regional climate changes and the varying socio-economic characteristics of affected places. Climate change also impacts the management of cultural heritage sites. We then propose a method to identify heritage sites at risk from climate change issues, considering the single components of risk, namely both the natural hazard and the resilient/vulnerable local characteristics that may increase or mitigate the potential damage. The link between the natural and human spheres is, in fact, crucial for the development of suitable mitigation and prevention strategies, particularly relevant for developing countries that suffer from scarce economic resources. Overall, this work aims to provide a method to detect heritage sites at risk and a decision support tool for strategically managing cultural sites. We focus on Italy since it is one of the countries with a higher endowment of cultural heritage. However, thanks to its high replicability, this tool might be exported and adopted in different contexts and scenarios. © 2025 The Authors

关键词： Climate change Cultural heritage Impacts Risks

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CorPN: Corner Prediction Network for Visual Tracking

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Qun Zhang, Haijun Yang, Kai Dai, Wei Ma, Jianghong Liu, Yuping the Department of Computer Science Harbin Institute of Technology Shenzhen Xili University Town Shenzhen518055 China the School of Computer Science and Artificial Intelligence WuHan Textile University Wuhan430200 China the School of Information and Control Engineering Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China the School of Microelectronics Fudan University Shanghai200433 China

Object tracking is a well-established task in the field of computer vision. Despite numerous efforts over the last few decades, object tracking remains a formidable challenge, largely due to the complexity of the scenes encountered in video sequences. In line with the powerful capabilities of Transformers for object feature extraction and fusion, as well as drawing inspiration from CornerNet, this paper introduces a new Transformer-based anchor-free method designed for object tracking called CorPN (i.e., Corner Prediction Network). Specifically, a Transformer-based backbone is proposed to extract and fuse deep features in a Siamese network architecture. Furthermore, our methodology aims to emulate the intuitive cognitive process by which humans identify the bounding-box corners of objects. CorPN turns out to be a more effective and robust tracking method because it jointly leverages multi-level feature aggregation, corner prediction, and probabilistic prediction incorporated in the tracking head. In extensive evaluations on public datasets, our method achieves a state-of-the-art performance. In particular, our approach achieves an area-under-the-curve (AUC) of 85.2%, a precision score (P) of 84.5%, and a normalized precision score (Pnorm) of 89.6%, outperforming extant state-of-the-art trackers on these metrics. © 2025 IEEE. All rights reserved,

关键词： Object detection

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Editorial: Biomedical signals and artificial intelligence towards smart robots control strategies

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Frontiers in Neurorobotics 2025年 19卷 1570870页

作者： Seddik, Hassene Fourati, Hassen Ben Jabeur, Chiraz Khraief, Nahla Department of Computer and Embedded Systems Higher National Engineering School of Tunis Tunis Tunisia Department of Automatic Control Université Grenoble Alpes Auvergne-Rhone-Alpes Saint Martin d'Héres France Ariana Tunisia Tunis Tunisia

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

引用

58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michał Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Paweł Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： acoustics mel-frequency cepstral coefficient mfcc sound spectrogram vehicle type detection vehicle type recognition

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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Reduced-Order Consensus Protocols with Pure Relative Output: Design, Existence, and Dual Results

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IEEE Transactions on Automatic control 2025年

作者： Ma, Yuwen Li, Xianwei Li, Shaoyuan Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai200240 China State Key Laboratory of Submarine Geoscience China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

This paper studies reduced-order dynamic consensus protocols for homogeneous linear multi-agent systems using pure relative output information. By applying H∞ control theory, a separation principle-like method with an additional small-gain constraint is proposed for designing an (nx-nu)th order protocol, where nx and nu are the numbers of states and inputs of each agent, respectively. Existence conditions for the protocol are then systematically discussed from the graph, low gain and high gain perspectives, respectively. It is revealed that the protocol always exists and can be found by the proposed design method for three cases: 1) directed acyclic networks, 2) agents with all eigenvalues in the closed left-half complex plane and 3) agents of minimum phase. Another reduced-order protocol of (nx - ny)th order is further discussed through duality. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Robust control

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H observer-based control for fuzzy Hadamard fractional-order systems

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Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

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