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Input-Restricted Stability of Continuous and Discrete Time Nonlinear Feedback systems

IEEE Transactions on Automatic control 2025年第5期70卷 3149-3164页

作者： Hilborne, Peter Lanzon, Alexander Khong, Sei Zhen University of Manchester Control Systems Centre Department of Electrical and Electronic Engineering School of Engineering ManchesterM13 9PL United Kingdom National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan

In this article, we develop the concept of input-restricted stability, which determines whether a feedback interconnection remains stable only for inputs in a given subset of all possible inputs in a specified signal space. Graph separation concepts and continuity are employed to derive an input-restricted feedback stability theorem, which guarantees input-restricted stability of a feedback interconnection if both systems in the interconnection fulfil some given criteria related to their input–output relationships. Significantly, this result is applicable to both continuous and discrete time systems, unlike many existing local stability results. This theorem is then specialized into simpler-to-compute corollaries and expanded to additional theorems which provide useful additional insights. This article ends with two salient specializations of key results developed herein: one is a type of input-restricted small-gain stability theorem with one system bounded by a linear gain and the other by a quadratic gain;and the other is a type of input-restricted passivity theorem. For both of these specializations, which are not stable for all energy bounded inputs, an example is provided where the feedback interconnection is shown to be stable when the energy of exogenous inputs is below a given threshold. © 1963-2012 IEEE.

关键词： Continuous time systems

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Deterministic Learning-Based Tracking control for Parabolic PDE systems With Infinite-Dimensional Nonlinear Uncertain Dynamics

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IEEE Transactions on Automatic control 2025年第5期70卷 3272-3287页

作者： Zhang, Jingting Yuan, Chengzhi Wu, Fen Wang, Cong Cheng, Hong University of Electronic Science and Technology of China Center for Robotics School of Automation Engineering Chengdu611731 China University of Rhode Island Mechanical Industrial and Systems Engineering Department KingstonRI02881 United States North Carolina State University Mechanical and Aerospace Engineering Department RaleighNC27695 United States Shandong University School of Control Science and Engineering Jinan250061 China

In this article, we investigate the joint problem of dynamics learning and tracking control for a class of parabolic partial differential equation (PDE) systems with infinite-dimensional uncertain nonlinear dynamics. A new learning control scheme is proposed based on the deterministic learning (DL) theory. One key feature of the proposed scheme is its capability of accurately learning the system's nonlinear uncertain dynamics during real-time tracking control with provable stability and convergence of the overall PDE closed-loop system. Specifically, the Galerkin method is first employed to deal with the infinite dimensionality of the PDE system;a novel DL-based adaptive learning control scheme is then proposed using dual radial basis function neural networks (RBF NNs), in which a pair of RBF NNs are employed to address, respectively, the matched and unmatched components of uncertain nonlinear system dynamics. This control scheme is finally examined on the original PDE system, and it is rigorously proved that: first the PDE system's state tracks the prescribed reference trajectory with guaranteed closed-loop stability and tracking accuracy;and second locally accurate identification of the PDE system's dominant nonlinear uncertain dynamics can be achieved with provable convergence of associated NN weights to their optimal values, thereby the learned knowledge can be ultimately stored and represented by the convergent constant RBF NN models. Based on this, an experience-based control scheme is further proposed, which is capable of recalling the associated learned knowledge in real-time to further improve control performance and reduce computational complexity with maintained provable stabilization. It is worth stressing that although this work is focused particularly on parabolic PDE systems, it is groundbreaking with important technical breakthroughs that would facilitate a more complete extension of the DL theory from traditional ordinary differential equation syste

关键词： Galerkin methods

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Robust Adaptive Sliding Mode Security control of Markov Jump Cyber-Physical systems With Stochastic Injection Attacks Through Event-Triggered-Based Observer Approach

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3679-3692页

作者： Jiang, Baoping Niu, Fuzhou Wu, Zhengtian Qiu, Jianbin Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China Politecnico di Milano Department of Mechanical Engineering Milan20156 Italy Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

This article addresses the challenge of state observer design for sliding mode security control in Markov jump cyber-physical systems subjected to stochastic injection attacks. To enhance network efficiency, a dynamic event-triggered algorithm is introduced in the communication channel. First, the design begins with a Luenberger state observer featuring an adaptive compensator. This configuration aims to effectively counteract malicious attacks. Second, an integral sliding hyperplane is formulated within the estimation space, which serves as the foundation for deriving the sliding mode dynamics, ensuring robustness against disturbances. Recognizing the diversity of transition rates (TRs), an elastic sliding mode controller is designed to accommodate three distinct types of TRs, which is also strategically designed to guarantee reachability and maintain sliding motion. Third, stochastic stability with an H∞ attenuation level is conducted separately for each type of TR. Correspondingly, the development of an algorithm for determining threshold parameters in triggered conditions is presented. Simultaneously, a proof of the nonexistence of Zeno behavior is provided, ensuring the stability and efficiency of the proposed system. Finally, a simulation study using a practical model is included to empirically demonstrate the validity of the proposed method in a real-world context. © 2013 IEEE.

关键词： Stochastic systems

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Distributed Resilient control of Cyber-Physical Multi-Agent systems Under Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 241-250页

作者： Xu, Yong Zhang, Wenyu Zhang, Yifang Wu, Zheng-Guang Beijing Institute of Technology School of Automation Beijing100081 China The University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

This paper delves into the distributed resilient state estimation-based secure control in multi-agent systems under false-data injection attacks. Firstly, we propose a novel adaptive distributed output observer approach that is based on resilient outputs. The key feature of our proposed observer is its sole dependence on the coefficients of the minimal polynomial of the leader's system matrix, rather than on individual entries of the leader's system. Subsequently, to identify the target attacker, we assign both trust and confidence values to each agent and its neighbors, thereby enabling attack detection and localization. Furthermore, we propose a distributed finite-time resilient control strategy, grounded in trust and confidence, to safeguard against attackers while ensuring secure output estimation and maintaining a bounded upper limit for finite-time convergence. Additionally, we introduce a piecewise function-based approximation method to achieve a less conservative upper bound for the convergence time, compared to traditional methods. Finally, we provide a numerical example to demonstrate the effectiveness of our proposed theoretical analysis. © 2023 IEEE.

关键词： Cyber attacks

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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A Comparative Study of Norm-Based and Norm-Free Event-Triggered control Architectures

A Comparative Study of Norm-Based and Norm-Free Event-Trigge...

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

作者： Kurtoglu, Deniz Yucelen, Tansel Tran, Dzung Casbeer, David Garcia, Eloy Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems University of South Florida TampaFL33620 United States Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Wepresent a comparative study of traditional "norm-based" and recently developed "norm free" event-triggered control architectures. For this purpose, the benchmark problem of scheduling control data transmissions from an embedded processor (i.e., controller) to a physical plant (i.e., dynamical system) is considered. Both architectures leverage the Lyapunov stability theory in order to address this problem by determining the timing of control data transmissions through their respective event-triggering rules. When the goal of utilizing an event-triggering approach is to reduce the number of data transmissions without sacrificing much from the closed-loop system performance, the norm-free architec ture results in fewer events and, therefore, allows for a greater reduction in control data transmissions. A detailed numerical study is conducted to evaluate these architectures in both open-loop stable and unstable linear time-invariant dynamical systems. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Closed loop control systems

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Robust Path-following controller for Multirotor Vehicles in Uncertain Wind Conditions

Robust Path-following Controller for Multirotor Vehicles in ...

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

作者： Bullock, John L. Trujillo, Anna C. Cheng, Sheng Hovakimyan, Naira Dynamic Systems and Control Branch NASA Langley Research Center HamptonVA23681 United States Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper presents simulations of L1 adaptive controller on a multirotor vehicle for compensation of convective winds. There are increasing efforts to utilize unmanned vehicles for fighting wildfires, but such vehicles need to be able to compensate for convective updrafts and other uncertainties (e.g., wind shear and turbulence) caused by the wildfire. Other application domains, such as urban air mobility, also must account for convective or uncertain winds present in urban microclimates. Geometric controllers have great tracking performance properties in environments without uncertainties, but the tracking performance degrades when wind disturbances and external loads are present in the environment. Introducing L1 adaptive control into the controller architecture compensates for these wind disturbances and reduces the tracking error caused by such disturbances. The controller architecture is implemented on a small quadrotor. The simulations demonstrate the advantages of the L1 adaptive augmentation for a sample figure-eight trajectory while encountering unknown variable wind conditions with an external load. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Adaptive control systems

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Nonlinear Infinite Dimensional Model for a Two Degree-of-Freedom Flexible Wing

Nonlinear Infinite Dimensional Model for a Two Degree-of-Fre...

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

作者： Bharadwaj, Shreyas N. Natarajan, Vivek Paranjape, Aditya A. Indian Institute of Technology Bombay Department of Aerospace Engineerin Powai Mumbai400076 India Indian Institute of Technology Bombay Systems and Control Engineering Powai Mumbai400076 India Monash University Mechanical and Aerospace Engineering ClaytonVIC3168 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Aninfinite-dimensional nonlinear model for a two-degree-of-freedom highly flexible wing is presented in this paper. The model describes the coupled dynamics of bending and torsion in terms of a set of nonlinear partial differential equations. When torsion is ignored, the resulting transverse bending equations are identical to those for the well-known elastica. The in vacuo response and the flutter onset characteristics of the model are compared with standard aeroelastic models using numerical simulation. The nonlinear model presented here could potentially serve as a benchmark for PDE-based control design methods, providing an intermediate step between low-fidelity linear models and nonlinear simulation models whose high fidelity is usually accompanied by large computational times. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Flexible wings

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms 4

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Narejo, Sanam Khan, Muhammad Taimoor Shaikh, Muhammad Zakir Dhirani, Lubna Luxmi Chowdhery, Bhawani Shankar Mehran Univerity of Engineering and Technology Department of Computer Systems Jamshoro Pakistan Mehran Univerity of Engineering and Technology Jamshoro National Centre for Robotics Automation and Artificial Intelligence Pakistan University of Malaga Mechanical Engineering and Energy Efficiency School of Industrial Engineering Malaga Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798331518882

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car's navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car's driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car's evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations. © 2025 IEEE.

关键词： Reinforcement learning

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Guest editorial: Digitizing food supply chains: a path to ensuring food security

International Journal of Industrial Engineering and Operatio...

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International Journal of Industrial engineering and Operations Management 2025年第2期7卷 97-99页

作者： Jagtap, Sandeep Trollman, Hana Woolley, Elliot Division of Engineering Logistics Lund University Lund Sweden Sustainable Manufacturing Systems Centre Cranfield University Cranfield United Kingdom Department of Management Centre for Sustainable Organisations School of Business University of Leicester Leicester United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom

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