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Impulsive Consensus of MASs With Input Saturation and DoS Attacks

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 414-424页

作者： Xuyang Wang Dengxiu Yu Xiaodi Li the Unmanned System Research Institute Northwestern Polytechnical University IEEE the School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University the School of Mathematics and Statistics Shandong Normal University the Center for Control and Engineering Computation Shandong Normal University

This paper investigates the secure impulsive consensus of Lipschitz-type nonlinear multi-agent systems(MASs) with input saturation. According to the coupling of input saturation and denial of service(DoS) attacks, impulsive control for MASs becomes extremely challenging. Considering general DoS attacks,this paper provides the sufficient conditions for the almost sure consensus of the MASs with input saturation, where the error system can achieve almost sure local exponential *** linear matrix inequalities(LMIs), the relation between the trajectory boundary and DoS attacks is characterized, and the trajectory boundary is estimated. Furthermore, an optimization method of the domain of attraction is proposed to maximize the size. And a non-conservative and practical boundary is proposed to characterize the effect of DoS attacks on MASs. Finally, considering a multi-agent system with typical Chua's circuit dynamic model, an example is provided to illustrate the theorems' correctness.

关键词： Almost sure consensus denial of service (DoS) attacks impulsive control input saturation

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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GLOBAL INTERNAL STABILIZABILITY DOES NOT IMPLY GLOBAL EXTERNAL STABILIZABILITY FOR SMALL SENSOR DISTURBANCES

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IEEE TRANSACTIONS ON AUTOMATIC control 1995年第12期40卷 2119-2122页

作者： FREEMAN, R Center for Control Engineering and Computation Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

It was shown in [1] that for control-affine nonlinear systems, global asymptotic stabilizability via continuous state feedback implies global input-to-state stabilizability with respect to actuator disturbances. We show that the analogous result for sensor disturbances does not hold: we construct a second-order system which admits a continuous memoryless globally asymptotically stabilizing state feedback controller but for which no such controller can prevent finite escape times from every initial condition in the presence of small sensor disturbances.

关键词： Asymptotic stability Actuators Sensor systems control systems Nonlinear systems Nonlinear control systems State feedback Robust stability control theory Working environment noise

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ADAPTIVE-control OF A CLASS OF NONLINEAR DISCRETE-TIME-SYSTEMS

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INTERNATIONAL JOURNAL OF control 1995年第2期62卷 303-324页

作者： YEH, PC KOKOTOVIC, PV Center for Control Engineering and Computation University of California Santa Barbara CA 93106 U.S.A.

We consider adaptive control via state feedback for a class of feedback linearizable discrete-time systems. To parallel the development in adaptive nonlinear continuous-time control, we employ a systematic procedure to design the controllers and the update laws for the so-called parametric-strict-feedback form and the parametric-pure-feedback form. The geometric conditions that characterize this class of systems are given. Depending on the growth conditions of the nonlinearities, global boundedness and convergence are achieved with various update laws. We also develop adaptive indirect schemes with an observer-based identifier for the parametric-strict-feedback form. As a result, the drawback of overparametrization in direct schemes can be removed.

关键词： Adaptive control systems

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Tracking controllers for systems linear in the unmeasured states

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AUTOMATICA 1996年第5期32卷 735-746页

作者： Freeman, RA Kokotovic, PV [a] Center for Control Engineering and Computation Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 U.S.A.

We introduce a class of extended strict feedback nonlinear systems in which unmeasured states enter the system equations in a linear (affine) fashion. For such systems, we present a procedure for constructing dynamic partial state feedback controllers that achieve the global asymptotic tracking of given reference trajectories while maintaining internal boundedness. The controller dynamic order is equal to the number of unmeasured states. To illustrate our design procedure, we construct such a tracking controller for a nonlinear electromechanical rotor/platform system. Copyright (C) 1996 Elsevier Science Ltd.

关键词： nonlinear systems global stability tracking disturbance rejection dynamic output feedback Lyapunov methods

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Fundamental design tradeoffs in filtering, prediction, and smoothing

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IEEE TRANSACTIONS ON AUTOMATIC control 1997年第9期42卷 1240-1251页

作者： Goodwin, GC Seron, MM Centre for Integrated Dynamics and Control Department of Electrical and Computer Engineering University of Newcastle NSW Australia Center for Control Engineering and Computation University of California Santa Barbara CA USA

This paper examines fundamental design tradeoffs that apply to all linear filtering, prediction, and smoothing problems, We introduce sensitivity functions that quantify filter performance, and we show that their frequency response satisfies constraints imposed by unstable poles and nonminimum phase zeros of the system, The constraints allow one to determine, a priori, whether or not a desired filter performance is attainable and how different arrangements of the measurement system influence the achievable performance.

关键词： Bode limits linear filtering performance limits

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Analogue computation and applied mathematics training

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Mathematics and Computers in Simulation 1962年第2期4.0卷 76-77页

作者： De Winne, Arthur L. the Control Engineering Laboratory Analogue Computation Center University of Ghent Belgium

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Analysis and Design of Time-Delay Impulsive Systems Subject to Actuator Saturation

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 196-204页

作者： Chenhong Zhu Xiuping Han Xiaodi Li IEEE the School of Mathematics and Statistics Shandong Normal UniversityJi'nan 250014China the Center for Control and Engineering Computation Shandong Normal UniversityJi'nan 250014China

This paper investigates the exponential stability and performance analysis of nonlinear time-delay impulsive systems subject to actuator saturation. When continuous dynamics is unstable, under some conditions, it is shown that the system can be stabilized by a class of saturated delayed-impulses regardless of the length of input delays. Conversely, when the system is originally stable, it is shown that under some conditions, the system is robust with respect to sufficient small delayed-impulses. Moreover, the design problem of the controller with the goal of obtaining a maximized estimate of the domain of attraction is formulated via a convex optimization problem. Three examples are provided to demonstrate the validity of the main results.

关键词： Delayed impulses impulsive control impulsive disturbance nonlinear systems saturation

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Converse Lyapunov theorems and robust asymptotic stability for hybrid systems

Converse Lyapunov theorems and robust asymptotic stability f...

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2005 American control Conference, ACC

作者： Cai, Chaohong Teel, Andrew R. Goebel, Rafal Center for Control Engineering and Computation Department of Electrical and Computer Engineering University of California Santa Barbara

ISBN: (纸本)0780390989

We state results on the existence of smooth Lyapunov functions for hybrid systems whose solutions satisfy a class-K script L sign script L sign estimate with respect to two measures. The class-K script L sign script L sign estimate, a natural extension of class-K script L sign estimate, is in terms of the elapsed time and the number of jumps that have occurred. The main result is that a smooth Lyapunov function exists if and only if the class-K script L sign script L sign estimate is robust. In turn, sufficient conditions for robustness are given. Special cases include systems with compact attractors. Most of the results parallel, and unify, what has been developed previously for differential inclusions and difference inclusions. © 2005 AACC.

关键词： Hybrid computers

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CONTRACTION AND CONCENTRATION OF MEASURES WITH APPLICATIONS TO THEORETICAL NEUROSCIENCE

arXiv

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arXiv 2025年

作者： Betteti, Simone Bullo, Francesco Department of Information Engineering Università degli Studi di Padova Padova35131 Italy Center for Control Dynamical Systems and Computation University of California at Santa Barbara Santa BarbaraCA93106 IT United States

We investigate the asymptotic behavior of probability measures associated with stochastic dynamical systems featuring either globally contracting or Br-contracting drift terms. While classical results often assume constant diffusion and gradient-based drifts, we extend the analysis to spatially inhomogeneous diffusion and non-integrable vector fields. We establish sufficient conditions for the existence and uniqueness of stationary measures under global contraction, showing that convergence is preserved when the contraction rate dominates diffusion inhomogeneity. For systems contracting only outside of a compact set and with constant diffusion, we demonstrate mass concentration near the minima of an associated non-convex potential, like in multistable regimes. The theoretical findings are illustrated through Hopfield networks, highlighting implications for memory retrieval dynamics in noisy *** Codes 35Q84, 60G10 © 2025, CC BY.

关键词： Random processes

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