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ieee Texas Power and Energy conference (TPEC)

作者： Harison, Caleb Sun, Wei El Mezyani, Touria Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Univ North Florida Coll Engn Jacksonville FL USA

ISBN: (纸本)9798350331202

Accurate parameters of the inverter-based resources (IBR) model are critical for the design of effective control systems. Both physics-based and data-driven methods present unique advantages and challenges in IBR parameter predictions. This paper proposes a hybrid method that uses machine learning tools coupled with a physics-based model to generate predictions of the system outputs. The scientific machine learning (SciML)-based method reduces the data size requirements and training time of machine learning and the computational needs of traditional physics-based models. The proposed SciML method is accomplished in a novel way, wherein the loss equation of the machine learning techniques is modified to contain the known scientific equations for the system output. Simulation results on a buck converter demonstrate that the new method is able to produce more accurate predictions in less time than its non-assisted counterpart.

关键词： Buck converter Inverter-based resources Parameter prediction Scientific machine learning

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data-driven Adaptive Compensation in Droop-Operated Low-Voltage Microgrids 18

Data-Driven Adaptive Compensation in Droop-Operated Low-Volt...

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18th International conference on Probabilistic Methods Applied to Power systems (PMAPS)

作者： Trickett, Lucas Voon, Liming Watson, Jeremy Univ Canterbury Dept Elect & Comp Engn Christchurch New Zealand

ISBN: (纸本)9798350372793;9798350372786

AC microgrids typically use droop-operated gridforming inverters for voltage and frequency regulation as this allows stabilisation of the microgrid without communication. However, in cases with very low X/R line ratios, there is greater coupling between real and reactive power and stability issues can occur. This paper proposes the use of an adaptive data-driven compensator which operates in conjunction with droop-control. The proposed data-driven control uses an LQR formulation for improved performance, and is updated in real-time according to developed criteria in order to adapt to network changes. We demonstrate improved stability regions via a large-scale MATLAB/Simulink study of a two-inverter test system where the line and load parameters are varied randomly.

关键词： data-driven control convex programming grid-forming inverters microgrid control low-voltage

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FastFlow: Availability Aware Federated Attention-based Spatial-Temporal GNN for Big data-driven Traffic Forecasting 59

FastFlow: Availability Aware Federated Attention-based Spati...

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59th Annual ieee International conference on Communications (ieee ICC)

作者： Yun, Sheng Bhuiyan, Zakirul Alam Su, Shen Sadi, Md. Taufiq Al Hasib Fordham Univ Dept Comp & Informat Sci New York NY 10458 USA Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China North South Univ Dept Elect & Comp Engn Dhaka Bangladesh

ISBN: (纸本)9781728190549

The rapid evolution of Intelligent Transportation systems (ITS) in the Big data era, propelled by the Internet of Things (IoT), has led to advanced data-driven vehicle traffic forecasting. Graph Neural Networks (GNNs), particularly the Attention-Based Spatial-Temporal Graph Neural Networks (ASTGNN), are promising in traffic forecasting but face limitations in integrating Big data with privacy-preserving Federated learning (FL) due to unique data topology processing. This paper introduces the Availability Aware Federated Attention-based Spatial-Temporal Graph Neural Network (FastFlow), an innovative framework that enhances ASTGNN by integrating Federated learning across entities and employing Big data methodologies. FastFlow's distinctiveness lies in its availability-aware approach, aggregating adjacency matrices for global topology and utilizing a novel communication protocol that prioritizes data availability and correlation among organizations. Our evaluation of Caltrans Performance Measurement System (PEMS) data in a simulated setup demonstrates FastFlow's ability to balance predictive accuracy and data security in a multi-organizational context.

关键词： Internet of Things Attention-based Spatial-Temporal Graph Neural Network Availability Aware Federated learning Big data

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SIR-Aided Secure Transmission and Attack Detection for Security Management of Nonlinear Cyber-Physical System Using GRU Autoencoder

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 5529-5538页

作者： Wu, Shimeng Luo, Hao Jiang, Yuchen Zhang, Jiusi Tian, Jilun Yin, Shen Harbin Inst Technol Harbin 150001 Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China Harbin Inst Technol Control & Simulat Ctr Harbin Peoples R China Norwegian Univ Sci & Technol Fac Engn Dept Mech & Ind Engn N-7491 Trondheim Norway

This article designs a data-driven unsupervised defense scheme for nonlinear systems by proposing a machine learning approach called gate recurrent unit-based modified denoising and stable image representation-aided autoencoders. The proposed scheme decomposes original data into two subspaces through orthogonal projection. For secure transmission, information related to the system's dynamics, which is in the image space of the controlled system, is hidden through filtering, whereas only the dynamic-independent information is plaintext for transmission, which supplements the cryptographic encryption methods from a control perspective. Moreover, attack detection for nonstealthy and stealthy attacks is achieved simultaneously under the same framework. A case study is conducted for validation on the a hardware-in-the-loop platform with a mecanum-wheeled vehicle. The comparative experiments with well-known unsupervised data-driven methods show the high detection accuracy of the proposed defense scheme for nonstealthy and stealthy attacks and the excellent encryption capability.

关键词： Cyber-physical system (CPS) attack detection CPS security management gate recurrent unit autoencoder secure transmission system image representation

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Reinforcement learning-Aided Performance-driven Fault-Tolerant control of Feedback control systems

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ieee TRANSACTIONS ON AUTOMATIC control 2022年第6期67卷 3013-3020页

作者： Hua, Changsheng Li, Linlin Ding, Steven X. Univ Duisburg Essen Inst Automat Control & Complex Syst AKS D-33415 Verl Germany Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

This article is concerned with a fault-tolerant control (FTC) scheme for feedback control systems with multiplicative faults by optimizing system performance with the aid of a reinforcement learning (RL) approach. To be specific, initially, based on the Youla-Kucera (YK) and dual YK parameterizations, a new performance-driven FTC method is proposed and its capability in dealing with multiplicative faults is proven. Then, data-driven implementation of this method using RL is elaborated. This implementation shows that RL can be applied efficiently by utilizing both plant model and data to recover the fault-induced system performance degradation. Finally, a benchmark study on an inverted pendulum system demonstrates the application of the proposed performance-driven FTC method.

关键词： Degradation System performance Fault tolerant systems Fault tolerance Trajectory Stochastic processes Estimation data-driven fault-tolerant control (FTC) performance degradation recovery reinforcement learning (RL)

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Task-driven Domain-Agnostic learning with Information Bottleneck for Autonomous Steering

Task-Driven Domain-Agnostic Learning with Information Bottle...

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ieee International conference on Robotics and Automation (ICRA)

作者： Shen, Yu Zheng, Laura Zhou, Tianyi Lin, Ming C. Univ Maryland Dept Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9798350384581;9798350384574

Environments for autonomous driving can vary from place to place, leading to challenges in designing a learning model for a new scene. Transfer learning can leverage knowledge from a learned domain to a new domain with limited data. In this work, we focus on end-to-end autonomous driving as the target task, consisting of both perception and control. We first utilize information bottleneck analysis to build a causal graph that defines our framework and the loss function;then we propose a novel domain-agnostic learning method for autonomous steering based on our analysis of training data, network architecture, and training paradigm. Experiments show that our method outperforms other SOTA methods.

关键词： Adversarial machine learning

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Safe control Against Uncertainty: A Comprehensive Review of control Barrier Function Strategies

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ieee systems MAN AND CYBERNETICS MAGAZINE 2025年第1期11卷 34-47页

作者： Wang, Shengbo Wen, Shiping Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Technol Sydney Australian AI Inst Fac Engn & Informat Technol Sydney NSW 2007 Australia

The growing interest in robust designs and data-driven technologies for safe control problems underscores the critical need to understand uncertainty for ensuring reliable safety guarantees. This review offers a concise survey of recent advancements in the control barrier function (CBF) method, widely recognized as a principled and effective approach to safe control, particularly in the context of uncertainty. From a unified perspective, we classify uncertainty into three types based on their learnability and transferability. Then we explore the techniques associated with each type of uncertainty found in the existing literature. Additionally, we highlight a knowledge-based safe control framework that utilizes meta-learning techniques to address dynamic uncertainty, shedding light on the potential for future investigations into practical learning algorithms and control problems. Furthermore, we employ topic modeling technologies to identify and generalize topics from the literature, thus revealing research trends and ongoing real-world applications withing the scope of safe control.

关键词： Metalearning Uncertainty Reviews Heuristic algorithms Knowledge based systems Machine learning Reliability engineering Market research Safety data models

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Bifurcation Analysis and Synchronization control of e-HR Neuron Model with Time Delay 13

Bifurcation Analysis and Synchronization Control of e-HR Neu...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Jiang, Jingna Sun, Dongsheng Cheng, Zunshui Shang, Yun Xin, Youming School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

Derived from the Hindmarsh-Rose (HR) neuron model, the extended Hindmarsh-Rose (e-HR) neuron model with time delay is constructed considering the effect of intracellular ion transfer and the inherent slow current delay on the change of neuron membrane potential. Firstly, the stability of the model is analyzed by using the Routh-Hurwitz criterion, and the conditions for the existence of Hopf bifurcation are derived by using the Hopf bifurcation theorem. Secondly, through numerical simulation, it is found that synchronization occurs in the limited range of delay value without control. Therefore, this paper introduces a nonlinear feedback control scheme, designed based on a Lyapunov-Krasovskii functional with a double integral term. The derived sufficient conditions for the synchronization of the master-slave neuron model with self-feedback delay are presented. Finally, the results of theoretical analysis are verified by numerical simulation and the effectiveness and viability of the controller are proved. © 2024 ieee.

关键词： Synchronization

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Direct data-driven control-Based Additive Feedforward Compensation for Fast and Precise Positioning control*

Direct Data-Driven Control-Based Additive Feedforward Compen...

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ieee/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Yamaguchi, Daigo Sato, Shimpei Maeda, Yoshihiro Nagoya Inst Technol Dept Engn Elect & Mech Engn Program Nagoya Aichi 4668555 Japan

ISBN: (纸本)9798350355376;9798350355369

In industrial positioning systems where rapid response and high-precision are crucial, minor model inaccuracies due to unknown dynamics and identification errors in controller design significantly impede achieving desired positioning accuracy. This paper introduces and evaluates a novel, direct data-driven control-based additive feedforward (FF) compensation method, aimed at enhancing precision in positioning while streamlining the design process. The purpose of this additive FF compensation is to attenuate undesirable error responses resulting from modeling errors in the existing model-based FF design. The proposed method enhances control performance by utilizing data-driven prediction of positioning response and optimizing the predicted response. Moreover, this work presents a newly developed design theory for the additive FF controller and highlights its design efficiency. The effectiveness of the proposed approach is substantiated through comprehensive experiments with a galvano scanner in printed circuit board laser drilling applications, demonstrating significant improvements in positioning accuracy and response time.

关键词： Feedforward control

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Optimization of Model Predictive control for Autonomous Vehicles Through learning-Based Weight Adjustment

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年

作者： Li, Haoran Lu, Yunpeng Li, Yaqiu Zheng, Sifa Sun, Chuan Zhang, Junyi Liu, Liqun Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan 430065 Peoples R China Tsinghua Univ Suzhou Automot Res Inst Suzhou 215134 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima 7390046 Japan Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Leeds Sch Comp Leeds LS2 9BW England

Model Predictive control (MPC) method is widely used in autonomous vehicle control technology. The adjustment of MPC weights is crucial for optimizing its control performance, ensuring precise and reliable operation. Traditionally, these weights are adjusted manually, which is inefficient. This study introduces a novel Butterfly Optimization Algorithm (BOA) learning-based method to determine the optimal MPC weights in an efficient way. By adopting the data-driven idea in machine learning, the trajectory data of field experiment human drivers is used to train the controller weights. A simulation-based training platform that enables the automatic training of the MPC controller with varying weights is also developed. Simulation results demonstrate the superior control accuracy and stability performance of BOA learning-based method compared to Linear Quadratic Regulator (LQR) and pure pursuit strategies. The findings suggest that the control method proposed in this research can significantly improve autonomous vehicle control performance and their reliability, thereby contributing to the advancement of autonomous driving technology.

关键词： Optimization Autonomous vehicles Mathematical models Vehicle dynamics Accuracy Predictive models Machine learning algorithms Model predictive control autonomous vehicle butterfly optimization algorithm machine learning control theory

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