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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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The Price of Simplicity: Analyzing Decoupled Policies for Multi-Location Inventory control

arXiv

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arXiv 2025年

作者： John, Yohan Shah, Vade Preiss, James A. Alizadeh, Mahnoosh Marden, Jason R. Center for Control Dynamical Systems and Computation The University of California Santa BarbaraCA United States

What is the performance cost of using simple, decoupled control policies in inherently coupled systems? Motivated by industrial refrigeration systems, where centralized compressors exhibit economies of scale yet traditional control employs decoupled room-by-room temperature regulation, we address this question through the lens of multi-location inventory control. Here, a planner manages multiple inventories to meet stochastic demand while minimizing costs that are coupled through nonlinear ordering functions reflecting economies of scale. Our main contributions are: (i) a surprising equivalence result showing that optimal stationary base-stock levels for individual locations remain unchanged despite the coupling when restricting attention to decoupled strategies;(ii) tight performance bounds for simple decoupled policies relative to optimal coupled policies, revealing that the worst-case ratio depends primarily on the degree of nonlinearity in the cost function and scales with the number of locations for systems with fixed costs;and (iii) analysis of practical online algorithms that achieve competitive performance without solving complex dynamic programs. Numerical simulations demonstrate that while decoupled policies significantly outperform their worst-case guarantees in typical scenarios, they still exhibit meaningful suboptimality compared to fully coordinated strategies. These results provide actionable guidance for system operators navigating the trade-off between control complexity and operational efficiency in coupled systems. © 2025, CC BY.

关键词： Stochastic systems

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Contractivity Analysis and control Design for Lur’e Systems: Lipschitz, Incrementally Sector Bounded, and Monotone Nonlinearities

arXiv

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arXiv 2025年

作者： Shima, Ryotaro Davydov, Alexander Bullo, Francesco Center for Control Dynamical Systems and Computation University of California at Santa Barbara Santa BarbaraCA93106 United States

In this paper, we study the contractivity of Lur’e dynamical systems whose nonlinearity is either Lipschitz, incrementally sector bounded, or monotone. We consider both the discrete- and continuous-time settings. In each case, we provide state-independent linear matrix inequalities (LMIs) which are necessary and sufficient for contractivity. Additionally, we provide LMIs for the design of controller gains such that the closed-loop system is contracting. Finally, we provide a numerical example for control design. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Policy for Optimal Avoidance of Multiple Engagement Zones

Policy for Optimal Avoidance of Multiple Engagement Zones

引用

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Milutinović, Dejan Von Moll, Alexander Weintraub, Isaac E. Casbeer, David Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider the scenario in the context of uncertainty. This uncertainty could be due to unknown wind or other external disturbances. The risk regions’ dynamics are tightly coupled to the state of the vehicle and as a result the task of navigating through a field dynamically changes as the vehicle traverses through it. Rather than using a nonlinear program solver, the approach taken here is one of formulating the problem as a stochastic optimal control problem wherein a feedback control law is derived. This feedback control law allows a single or multiple vehicles to reach the desired goal location efficiently and with minimal risk incursion. When inside a risk region, there is a probability rate of the vehicle being neutralized. The stochastic dynamical system is modeled as a hybrid system, i.e., regime switching diffusion. The hybrid system is comprised of continuous spatial dynamics and discrete operational states corresponding to "normal" and "neutralized". The optimal control problem is discretized into a field of vehicle states and control. Using Value Iteration, the control signal at each discrete location is optimized, providing a feedback control strategy over the field. Following a discussion of the methods and approaches, this problem is formulated, simulations are performed, and conclusions and remarks are made © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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Meaningful human control of partially automated driving systems: Insights from interviews with Tesla users

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 113卷 213-236页

作者： Suryana, Lucas Elbert Nordhoff, Sina Calvert, Simeon Zgonnikov, Arkady van Arem, Bart Department of Transport and Planning Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands Electric Vehicle Research Center Institute of Transportation Studies University of California Davis United States Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology Delft Netherlands Centre for Meaningful Human Control Delft University of Technology Delft Netherlands

Partially automated driving systems are designed to perform specific driving tasks—such as steering, accelerating, and braking—while still requiring human drivers to monitor the environment and intervene when necessary. This shift of driving responsibilities from human drivers to automated systems raises concerns about accountability, particularly in scenarios involving unexpected events. To address these concerns, the concept of meaningful human control (MHC) has been proposed. MHC emphasises the importance of humans retaining oversight and responsibility for decisions made by automated systems. Despite extensive theoretical discussion of MHC in driving automation, there is limited empirical research on how real-world partially automated systems align with MHC principles. This study offers two main contributions: (1) an empirical evaluation of MHC in partially automated driving, based on 103 semi-structured interviews with users of Tesla's Autopilot and Full Self-Driving (FSD) Beta systems;and (2) a methodological framework for assessing MHC through qualitative interview data. We operationalise the previously proposed tracking and tracing conditions of MHC using a set of evaluation criteria to determine whether these systems support meaningful human control in practice. Our findings indicate that several factors influence the degree to which MHC is achieved. Failures in tracking—where drivers' expectations regarding system safety are not adequately met—arise from technological limitations, susceptibility to environmental conditions (e.g., adverse weather or inadequate infrastructure), and discrepancies between technical performance and user satisfaction. Tracing performance—the ability to clearly assign responsibility—is affected by inconsistent adherence to safety protocols, varying levels of driver confidence, and the specific driving mode in use (e.g., Autopilot versus FSD Beta). These findings contribute to ongoing efforts to design partially automated driving

关键词： Human engineering

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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

引用

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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Probing Rotational Decoherence with a Trapped-Ion Planar Rotor

引用

Physical Review Letters 2025年第3期134卷 033601-033601页

作者： Neil Glikin Benjamin A. Stickler Ryan Tollefsen Sara Mouradian Neha Yadav Erik Urban Klaus Hornberger Hartmut Häffner Department of Physics University of California Berkeley Berkeley California 94720 USA Challenge Institute for Quantum Computation University of California Berkeley Berkeley California 94720 USA Institute for Complex Quantum Systems and Center for Integrated Quantum Science and Technology Ulm University Albert-Einstein-Allee 11 89069 Ulm Germany Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Present address: Exponent Inc. Electrical Engineering and Computer Science Practice Warrenville Illinois USA. University of Duisburg-Essen Faculty of Physics Lotharstraße 1 47057 Duisburg Germany

The quantum rotor is one of the simplest model systems in quantum mechanics, but only in recent years has theoretical work revealed general fundamental scaling laws for its decoherence. For example, a superposition of orientations decoheres at a rate proportional to the sine squared of the angle between them. Here, we observe scaling laws for rotational decoherence dynamics for the first time, using a 4 μm diameter planar rotor composed of two Paul-trapped ions. We prepare the rotational motion of the ion crystal into superpositions of angular momentum with well-defined differences ranging from 1−3ℏ, and measure the rate of decoherence. We also tune the system-environment interaction strength by introducing resonant electric field noise. The observed scaling relationships for decoherence are in excellent agreement with recent theoretical work, and are directly relevant to the growing development of rotor-based quantum applications.

关键词： Trapped ions

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Event-Triggered Optimal Adaptive control Algorithm for Continuous-Time Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 282-293页

作者： Kyriakos G.Vamvoudakis the Center for Control Dynamicalsystems and Computation(CCDC)University of California

This paper proposes a novel optimal adaptive eventtriggered control algorithm for nonlinear continuous-time *** goal is to reduce the controller updates,by sampling the state only when an event is triggered to maintain stability and *** online algorithm is implemented based on an actor/critic neural network structure.A critic neural network is used to approximate the cost and an actor neural network is used to approximate the optunal event-triggered *** in the algorithm proposed there are dynamics that exhibit continuous evolutions described by ordinary differential equations and instantaneous jumps or impulses,we will use an impulsive system approach.A Lyapunov stability proof ensures that the closed-loop system is asymptotically ***,we illustrate the effectiveness of the proposed solution compared to a timetriggered controller.

关键词： Learning (artificial intelligence) Heuristic algorithms Neural networks Nonlinear systems Bandwidth Linear systems Adaptive control

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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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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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