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检索条件"任意字段=Conference on Performance and Control of Network Systems III"

共 37359 条记录，以下是31-40 订阅

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Single Model Scheme-based Smith Predictor for the Mitigation of the effects of network Imperfections in Consensus control of Multi Agent systems 10

Single Model Scheme-based Smith Predictor for the Mitigation...

引用

10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Solyman, Loaie Elbadawyl, Ayman Meroth, Ansgar German Univ Cairo Fac Engn & Mat Sci Mechatron Engn Dept Cairo Egypt Heilbronn Univ Automot Syst Engn Dept Heilbronn Germany

ISBN: (纸本)9798350373981;9798350373974

This paper addresses the problem of network modelling and its application in consensus control of networked Multi Agent systems (NMASs). These NMASs experience network imperfections such as variable time delays and packet dropouts, where some researchers sought a model that predicts the same behaviour of these imperfections. Earlier studies had two models predicting the behaviour of each network imperfection independently in a scheme called the Two Independent Model Scheme (TIMS). A new approach of modelling the behaviour of the network, based on the Semi-Continuous Hidden Markov Model (SCHMM), is introduced in this paper. This was achieved through introducing a Dirac-delta function alongside the Gaussian distributions in the SCHMM. The new model predicts time delays and packet dropouts, in a homogeneous manner, in a Single Model Scheme (SMS). A Smith Predictor (SP) scheme was implemented for the consensus control of NMASs to mitigate the effects of network limitations. The numerical analysis shows that the new model is more accurate in representing the network limitations, and shows that its performance is better at mitigating the effects of network imperfections in the consensus control problem.

关键词： Delta functions

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Model Predictive control for Offset-free Tracking: a new data-driven application for Secondary Voltage Regulation on Transmission network

Model Predictive Control for Offset-free Tracking: a new dat...

引用

International conference on Smart Energy systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Acerbi, Federica Piccagli, Davide Stefano Pozzi, Fabio Sciarpa, Luca Giannuzzi, Giorgio Maria Pisani, Cosimo Bruno, Gianluca CESI SpA Milan Italy Terna SpA Rome Italy

ISBN: (纸本)9798350386509;9798350386493

This paper investigates the evolution of Secondary Voltage Regulation (SVR) in response to the increasing penetration of enewable energy sources (RES) within power systems. Traditional SVR, historically reliant on fossil fuel-based plants, faces challenges stemming from their under-utilization and intermittent operation. As conventional power plants decline, emerging resources such as synchronous compensators and STATCOMS offer voltage control capabilities without compromising RES integration, prompting the need for a redesigned control system to effectively harness their capabilities and optimize voltage regulation performance in an increasingly dynamic network environment. In this evolving scenario, a review of SVR literature reveals a shift towards data-driven methodologies, leveraging real-world data for improved control strategies. To address these challenges, a new Secondary Voltage Regulator is proposed based on a data-driven Model Predictive control (MPC) approach, designed for offset-free tracking. The suggested approach, known for its proficiency in tracking, has been adjusted to provide an implementation suitable for the Italian transmission system. Field tests conducted on Sicilian transmission network validate the effectiveness of the MPC-based controller under real-world conditions, filling an important gap in understanding its performance and applicability in transmission systems.

关键词： Data-driven Modeling Model Predictive control Reactive Power Management Secondary Voltage control

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Security Tracking control for Discrete-Time Stochastic Delayed networked systems With Cyber-Attacks by Dynamic Event-Triggered Approach 36

Security Tracking Control for Discrete-Time Stochastic Delay...

引用

36th Chinese control and Decision conference (CCDC)

作者： Xu, Shiqiang Yang, Jun Wu, Qiong Southwest Minzu Univ Coll Elect Engn Key Lab Elect & Informat Engn State Ethn Affairs Chengdu Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The purpose of this paper is to develop a security tracking controller for discrete-time delayed stochastic network control systems (SNCSs) that are vulnerable to stochastic cyber attacks. The presented strategy, called the Dynamic Event-Triggered Communication Approach (DETCA), adapts the volume of data transmitted across the network based on fluctuations in tracking error system, while concurrently maintaining the desired level of the tracking performance. These stochastic cyber attacks encompass deception attacks and denial-of-service (DoS), which may occur during signal transmission over the network. Based on Lyapunov stability theory, a sufficient condition is provided for guaranteeing the asymptotic stability of the tracking error system. The linear matrix inequalities (LMIs) are employed to solve the event-triggering parameters and the tracking controller gain. The efficacy of the proposed theoretical results is lastly demonstrated by a simulation example provided.

关键词： Discrete-time stochastic delayed systems networked controll systems Dynamic event-triggered communication approach (DETCA) Cyber attacks

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Input-to-State Stable Coupled Oscillator networks for Closed-form Model-based control in Latent Space 38

Input-to-State Stable Coupled Oscillator Networks for Closed...

引用

38th conference on Neural Information Processing systems, NeurIPS 2024

作者： Stölzle, Maximilian Della Santina, Cosimo Department of Cognitive Robotics Delft University of Technology Netherlands

Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical systems remains an open challenge. We argue that a promising avenue is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping. We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems, (iii) these methods do not have an invertible mapping between input and latent-space forcing. This work proposes a novel Coupled Oscillator network (CON) model that simultaneously tackles all these issues. More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it possesses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments. Moving to the experimental side, we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images. An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training. We tackle (iii) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force. Finally, we leverage these three properties and show that they enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information. © 2024 Neural information processing systems foundation. All rights

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Anomaly Detection networks and Fuzzy control Modules for Energy Grid Management with Q-Learning-Based Decision Making

Anomaly Detection Networks and Fuzzy Control Modules for Ene...

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SIAM International conference on Data Mining (SDM)

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Yu, Philip S. Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci Bergen Norway Univ Illinois Dept Comp Sci Chicago IL USA

ISBN: (纸本)9781611977653

Renewable energy generation has attracted the interest of researchers, but it is volatile, and management systems are vulnerable to malicious attacks. Therefore, security issues are of paramount importance for energy management systems. In this paper, we propose a secure Q-learning-based energy network management system (SQEMS), which consists of an anomaly detection module, a fuzzy control module to mitigate attacks, and a decision-making module to manage the energy grid. Experimental results show that the proposed anomaly detection module has excellent performance on malicious suppliers attacks (MS), and the fuzzy control module can further mitigate the negative effects of false predictions. The robustness analysis shows the effectiveness, robustness, and transferability in anomaly detection and energy management.

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Research on the Physical Layer Vulnerability of Industrial control systems Based on Deterministic and Stochastic Petri Nets 24

Research on the Physical Layer Vulnerability of Industrial C...

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International conference on Algorithms, Software Engineering, and network Security (ASENS)

作者： Tang, Jin Shuang, Wei China Three Gorges Grp Corp Digital Management Ctr Wuhan 430000 Hubei Peoples R China China Three Gorges Corp Yangtze River Basin Hub Operat & Management Ctr Wuhan 430000 Hubei Peoples R China

ISBN: (纸本)9798400709784

With the development of modern industrialization, the network security issues of industrial control systems have become increasingly severe, necessitating enhanced research on the vulnerability mechanisms of industrial control network security. In response to the physical layer vulnerability issues in industrial control networks, a vulnerability analysis and performance quantification method based on deterministic and stochastic Petri net (DSPN) model is proposed. This method establishes a DSPN model using the Ethernet transmission mechanism as an example. Firstly, it qualitatively analyzes the possible abnormal states of the physical layer after being attacked. Then, it quantitatively analyzes the throughput and delay of the physical layer under abnormal states using DSPN tools and queuing theory. A simulation analysis was conducted on the throughput and transmission delay after the attack, and the result indicates that the Ethernet link negotiation mechanism is vulnerable. Timely wireless pulse injection can cause the port status to be abnormal. Although it does not lead to communication interruption, it will seriously affect communication performance.

关键词： Industrial control network Vulnerability analysis Deterministic and stochastic Petri nets Markov chain

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Decentralized Cooperative Multi-Agent Deep Reinforcement Learning for Real-Time Optimization of Emulated Ad-Hoc Radio networks

Decentralized Cooperative Multi-Agent Deep Reinforcement Lea...

引用

2024 Military Communications conference

作者： Moehlenhof, Thies Jansen, Norman Fraunhofer FKIE Informat Technol Command & Control ITF Wachtberg Germany

ISBN: (纸本)9798350374247;9798350374230

This research explores the application of policy gradient methods in multi-agent reinforcement learning, augmented with offline reinforcement Learning techniques. The goal is to leverage these methods to improve communication of military command and control information systems (C2IS) in real-time emulated radio networks by the use of agents deployed on each network node in a decentralized manner. These agents have the task of improving the performance of the network by learning to adapt the local C2IS and communication services on their designated nodes to the prevailing dynamic network conditions. The proposed method is assessed in an emulated environment, where agents are trained to augment the network performance by controlling the transmission rate of a set of Blue-Force Tracking Services within an emulated ad-hoc radio network in real-time.

关键词： Multi-Agent Reinforcement Learning network Optimization Ad-Hoc Radio network Emulation Real-Time Reinforcement Learning

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Research and Design of PID control System Based on BP Neural network 24

Research and Design of PID Control System Based on BP Neural...

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4th International conference on Artificial Intelligence, Automation and High performance Computing, AIAHPC 2024

作者： Shi, Wei Qingdao Institute of Technology China

ISBN: (纸本)9798400710049

As a widely adopted control strategy in industry, PID control is widely recognized for its simplicity, efficiency and reliability. However, in the face of complex and variable nonlinear industrial control systems, traditional PID controllers have many limitations such as insufficient adaptability, difficulty in parameter tuning, poor robustness, so that the control performance requirements in the actual project are often difficult to be satisfied by the existing way. In this paper, the application feasibility of artificial neural network algorithm in PID controller will be deeply discussed, and a control scheme combining BP neural network with PID control is proposed to realize online self-correction of PID parameters, and then improve the control effect of PID controller on complex nonlinear controlled systems. Practice has proved that in the BP neural network model, the number of nodes in the input layer corresponds to multiple input signals, and the number of nodes in the output layer corresponds to three parameters (Kp, Ki, Kd) of the PID controller. Through the simulation test results of MATLAB platform, the performance of PID control system based on BP neural network is better than that of traditional PID controller in overshoot, adjustment time and rising time, and the control performance is effectively improved. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Optimization of ReLU Activation Function for Deep-Learning-based Breast Cancer Classification on Mammogram Images

Optimization of ReLU Activation Function for Deep-Learning-b...

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IEEE International conference on Automatic control and Intelligent systems (I2CACIS)

作者： Razali, Noor Fadzilah Isa, Iza Sazanita Sulaiman, Siti Noraini Osman, Muhammad Khusairi Karim, Noor Khairiah A. Nordin, Siti Aminah Univ Teknol MARA Coll Engn Elect Engn Studies Permatang Pauh Campus George Town Malaysia Univ Sains Malaysia Bertam Adv Med & Dent Inst Dept Biomed Imaging George Town Malaysia Univ Teknol MARA Coll Engn Elect Engn Studies Pasir Gudang Campus Johor Baharu Malaysia

ISBN: (纸本)9798350372113;9798350372106

The use of the deep Convolutional Neural network (CNN) in breast cancer classification of mammogram images has been widely investigated to aid radiologists in better clinical diagnoses. Multiple levels of convolution and non-linearity repetitions in CNN's architecture are required to extract significant data to be represented. However, the vanishing gradient effect occurs when deeper network training as a product of the partial derivative of loss function on each weightage update can cause no meaningful network learning, even with additional epochs. Overcoming this using the activation function of rectified linear unit (ReLU) by allowing neurons to be activated to allow non-linearity when the output is more than zero could lessen the problem. However, restrictive allowance of non-linearity for <0 for final feature extraction when producing output probability on highly complex data such as mammogram images leads to dropped network performance. To overcome this, this study proposed an adaptive ReLU based on genetic algorithm (GA) profiling to determine the best threshold value for allowing neuron activation based on mutation and adaptation to improve the restrictive capability of the original ReLU. We modified the adaptive ReLU on the final learning layer of two CNN architectures and observed the performance on a public mammogram dataset of INbreast. Our experiments show improved accuracy from 95.0% to 98.5% and improved classification performance compared to other well-known activation functions. Applying evolutionary-based GAs to activation functions can represent an exciting frontier in meta-learning for neural networks.

关键词： activation function ReLU Convolutional neural network mammogram genetic algorithm optimization

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Explainable Intrusion Detection in Industrial control systems 7

Explainable Intrusion Detection in Industrial Control System...

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IEEE 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Eltomy, Reham Lalouani, Wassila Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)9798350363029;9798350363012

In the realm of smart manufacturing, Industrial control systems (ICSs) play a critical role in supervising essential operations across various manufacturing facilities. As ICSs become increasingly integral to critical infrastructure, their susceptibility to cyber threats escalates. Despite the positive impact of abundant digital data communication on ICS performance, rising concerns about data security underscore the pivotal role of intrusion detection systems (IDS) in averting the consequences of network security attacks. However, challenges persist, particularly in the detection of zero-day attacks. Furthermore, while many IDSs exhibit high performance for known attack patterns, they often function as black boxes, presenting interpretability challenges for network operators to take appropriate actions. This paper introduces a novel zero-day intrusion detection system, thoughtfully crafted to address the interdependence within ICS network traffic. Our approach designs a multi-head-attention mechanism, not only for precise classification of network traffic but also to mitigate zero-day attacks. Subsequently, we incorporate a novel autoencoder architecture, purposefully designed to capture distinctive patterns present in both known attacks and normal traffic, leveraging the output from the attention layer. Furthermore, our approach integrates Explainable Artificial Intelligence mechanisms, employing the multi-head attentions model to offer a more detailed description of the underlying process involved in traffic categorization. The model's efficacy is demonstrated through experiments conducted using water system ICS testbeds, underscoring its performance and reliability in detecting diverse cyber-attacks and inferring zero-day attacks with an accuracy surpassing 89%, while also providing interpretable results.

关键词： Industrial control systems Zero-day attacks Intrusion detection SCADA systems

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