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2024 IEEE International performance, Computing, and Communications conference, IPCCC 2024

作者： Laidig, Robin Herrmann, Jona Augustat, David Durr, Frank Rothermel, Kurt University of Stuttgart IPVS Stuttgart Germany

ISBN: (纸本)9798350367942

Since network delays can severely impact networked control systems (NCS), both guaranteed Quality of Service (QoS) at the network level and guaranteed stability at the application level in the presence of delays are essential. The recently developed Dynamic Priority Token Bucket (DPTB) aims to meet network-level requirements through dynamic yet deterministic latency bounds that depend on the application's sending *** concept provides great potential for a combined design method with NCS controllers, which we propose in this paper. We design and implement two approaches that combine DPTB with (1) the well-known robust input/output (DPTB-RobustIO) controller to provide provable stability and (2) a novel multi-level linear quadratic regulator (DPTB-MLQR) to improve control *** the evaluation of the approaches, an Ethernet-based NCS with a Linux software switch and an inverted pendulum is used. This benchmark setup for fully automated evaluations of co-designs that combine network scheduling and QoS mechanisms with NCS controllers running on real hardware components is made available as an open source implementation. DPTB-RobustIO and DPTB-MLQR reduced the data rate by up to 26% and increased the control performance by 27% compared to regular token buckets with RobustIO control. © 2024 IEEE.

关键词： Pendulums

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Predefined-Time Command Filtered control for Tracking and Synchronization of Multi-Motor Servo systems With Fuzzy Approximator

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2025年第3期72卷 494-498页

作者： Wang, Xiang Wang, Baofang Yu, Jinpeng Nanjing Inst Technol Sch Automat Nanjing 211167 Peoples R China Nanjing Inst Technol Jiangsu Collaborat Innovat Ctr Smart Distribut Net Nanjing 211167 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Qingdao Univ Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China

This brief proposes a predefined-time command filtered control strategy for multi-motor servo systems to facilitate high-performance tracking and synchronization. For load tracking, a predefined-time control approach is integrated within the backstepping framework, which accelerates the convergence rate and simplifies the adjustment of the convergence time. Command filters are utilized to obtain the derivatives of virtual control signals, and a compensation mechanism is developed to reduce filtering errors. Furthermore, a fuzzy logic system is constructed to estimate and counteract the frictional effect. For motor synchronization, a practical grouping control approach is designed for four motors, with control inputs overlaid on command signals of motor currents to rapidly realize synchronization of motor speeds. The predefined-time stability of the closed-loop system is demonstrated. The practicality and effectiveness of the proposed strategy are validated by experimental results.

关键词： Servomotors Synchronization Convergence Circuits Torque Gears Fuzzy logic Backstepping Technological innovation Lyapunov methods Multi-motor servo system command filtered backstepping tracking control synchronization control

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Design of Hybrid Neural controller for Nonlinear MIMO System Based on NARMA-L2 Model

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IETE JOURNAL OF RESEARCH 2023年第5期69卷 3038-3051页

作者： El Hamidi, K. Mjahed, M. El Kari, A. Ayad, H. El Gmili, N. Cadi Ayyad Univ Lab Elect Syst Energy Efficiency & Telecommun BP 549Av Abdelkarim Elkhattabi Gueliz 4000 Marrakesh Morocco Royal Sch Aeronut Math & Syst Dept Marrakech 40000 Morocco Univ Hassan II Casablanca Condensed Matter Phys & Renewable Energy Lab Mohammedia BP 146 Mohammadia 28806 Morocco

This paper introduces a nonlinear adaptive controller of unknown nonlinear dynamical systems based on the approximate models using a multi-layer perceptron neural network. The proposal of this study is to employ the structure of the Multi-Layer Perceptron (MLP) model into the NARMA-L2 structure in order to construct a hybrid neural structure that can be used as an identifier model and a nonlinear controller for the MIMO nonlinear systems. The big advantage of the proposed control system is that it doesn't require previous knowledge of the model. Our ultimate goal is to determine the control input using only the values of the input and output. The developed NARMA-L2 neural network model is tuned for its weights employing the backpropagation optimizer algorithm. Nonlinear autoregressive-moving average-L2 (NARMA-L2) neural network controller, based on the inputs and outputs from the nonlinear model, is designed to perform control action on the nonlinear for the attitude control of unmanned aerial vehicles (UAVs) model. Once the system has been modeled efficiently and accurately, the proposed controller is designed by rearranging the generalized submodels. The controller performance is evaluated by simulation conducted on a quadcopter MIMO system, which is characterized by a nonlinear and dynamic behavior. The obtained results show that the NARMA-L2-based neural network achieved good performances in modeling and control.

关键词： Back propagation algorithm MIMO system MLP neural network NARMA-L2 quadcopter

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SDN-enabled Quantized LQR for Smart Traffic Light controller to Optimize Congestion

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2024年第1期24卷 1-25页

作者： Sachan, Anuj Kumar, Neetesh Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

Existing intersection management systems, in urban cities, lack in meeting the current requirements of selfconfiguration, lightweight computing, and software-defined control, which are necessarily required for congested road-lane networks. To satisfy these requirements, this work proposes effective, scalable, multi-input and multi-output, and congestion prevention-enabled intersection management system utilizing a softwaredefined control interface that not only regularly monitors the traffic to prevent congestion for minimizing queue length and waiting time but also offers a computationally efficient solution in real-time. For effective intersection management, a modified linear-quadratic regulator, i.e., Quantized Linear Quadratic Regulator (QLQR), is designed along with Software-defined networking (SDN)-enabled control interface to maximize throughput and vehicles speed and minimize queue length and waiting time at the intersection. Experimental results prove that the proposed SDN-QLQR improves the comparative performance in the interval of 24.94%-49.07%, 35.78%-68.86%, 36.67%-59.08%, and 29.94%-57.87% for various performance metrics, i.e., average queue length, average waiting time, throughput, and average speed, respectively.

关键词： Intelligent transportation system (ITS) smart city traffic light controller (TLC) urban traffic network

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A Hybrid Monotonic Neural network Approach for Multi-Area Power System Load Frequency control Against FGSM Attack

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2024年第8期71卷 3780-3784页

作者： Liu, Xinghua Jiao, Qianmeng Qiao, Siwei Yan, Ziming Wen, Shiping Wang, Peng Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Technol Sydney Australian AI Inst Fac Engn & Informat Technol Sydney NSW 2007 Australia

The integrity of data-driven load frequency control (LFC) in power system is increasingly threatened by adversarial attack. Addressing this concern, this brief introduces a novel hybrid approach that integrates adversarial reinforcement learning and monotonic neural network (ARL-HMNN) for LFC in multi-area power system. To holistically counter unforeseen uncertainties and to withstand the prevalent adversarial attack, the proposed ARL-HMNN approach builds a stable neural network structure with monotonicity constraints, and optimizes this neural network for LFC with adversarial training and deep deterministic policy gradient algorithm. By enforcing the deviation-command monotonicity constraints, the HMNN is enabled to satisfy Lyapunov stability conditions for LFC, which significantly enhances the stability and robustness of the power system. To further enhance the robustness of LFC, the classic fast gradient sign method (FGSM) adversarial attack is applied during the reinforcement learning training process. Through the integration of adversarial training, our method improves the system's resilience to FGSM attack under malicious threat from the communication network, while at the same time maintaining provable frequency stability. The superior performance of the developed approach is demonstrated by comparison to existing data-driven control methods on the IEEE 39-bus power system.

关键词： Power system stability Circuit stability Neural networks Training Cyberattack Reinforcement learning Lyapunov methods Load frequency control monotone neural network Lyapunov stability deep reinforcement learning cyber-attack

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Secure AI/ML-based control in Intent-based Management System

Secure AI/ML-based Control in Intent-based Management System

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4th IEEE Annual International conference on Cyber Security and Resilience (IEEE CSR)

作者： Karacay, Leyli Baktir, Ahmet Cihat Fuladi, Ramin Biyar, Elham Dehghan Tuna, Omer Faruk Arikan, Ipek Ericsson Res Istanbul Turkiye

ISBN: (纸本)9798350375367

The emergence of intent-based management (IbM) as a concept for managing and operating telecommunication systems in 6G necessitates the identification of potential threats and risks associated with its adoption and implementation. IbMs leverage automation, artificial intelligence (AI), and machine learning (ML) techniques to manage networks based on high-level and abstract definition of goals and requirements via intents. Although AI/ML models enable the IbM system to dynamically configure network parameters and resources to meet the intents, these models have some intrinsic vulnerabilities against adversarial attacks. In this work, we show the vulnerability of the IbM systems against adversarial attacks which might be originated through a compromised/malicious component in the system. Such attacks can degrade the performance of the IbM system and lead the system to take incorrect decisions. This would result in a propagated effect on the network performance and monetary costs for the operator as well. We further demonstrate the efficiency of the adversarial training as a protection scheme through experiments under various network configurations.

关键词： Intent-based management system adversarial attack AI/ML security

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Attention Guided Deep Neural network for Inspection of Surface Defects on Steel Products 24

Attention Guided Deep Neural Network for Inspection of Surfa...

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24th International conference on control, Automation and systems

作者： Jeong, Seyoung Song, Jimin Lee, Sang Jun Jeonbuk Natl Univ Div Elect Engn Jeonju 54896 South Korea

ISBN: (纸本)9798331517939;9788993215380

Recent advancements in deep learning, particularly in semantic segmentation, have achieved notable success in various industrial applications. However, the characteristics of images vary between applications, presenting distinct challenges. In the case of steel images, environmental factors during acquisition significantly affect their appearance, and the random occurrence of defects complicates their generalization, making defect segmentation for surface inspection particularly challenging. This paper introduces TAG-Net, a novel attention-based semantic segmentation network aimed at improving the distinction between background and defects in challenging input images. TAG-Net estimates three attention maps for the background, defects, and their boundaries, with boundary detection included as an auxiliary task to enhance the guidance of the attention maps. Experiments on the NEU-Seg dataset demonstrate that our proposed method significantly outperforms traditional baseline approaches for general images and recent approaches for steel images, yielding superior segmentation performance.

关键词： Steel manufacturing Surface defect identification Semantic segmentation Boundary segmentation Attention mechanisms

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Quadrotor control Design with Spiking Neural networks 24

Quadrotor Control Design with Spiking Neural Networks

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24th International conference on control, Automation and systems

作者： Kim, Donghyeong Lee, Changhyeon Han, Soohee POSTECH Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea POSTECH Pohang Univ Sci & Technol Dept Elect Engn & Convergence IT Engn Pohang 37673 South Korea

ISBN: (纸本)9798331517939;9788993215380

This study investigates the potential of spiking neural networks (SNNs) as a bio-inspired alternative to traditional Proportional (P) controllers in quadrotor simulations. A quadrotor model was developed and its performance validated through integration with SNNs, demonstrating the system's feasibility. The dataset for training the SNN was generated by applying the P controller to random states. The network fundamentally adopts a Convolutional Neural network structure but is uniquely modified to include spike current encoding and separated decoding, enhancing its processing capabilities. To address the inherent non-differentiable nature of SNNs, a surrogate gradient method was employed to facilitate effective backpropagation. The optimized SNN exhibited promising results, achieving an average loss of 0.251 and effectively managing to reposition the quadrotor to its original point. This study not only showcases the capabilities of SNNs in simulating and controlling flight dynamics but also paves the way for further research into their application in advanced control systems.

关键词： Spiking Neural networks(SNN) Bio-inspired Quadrotor Simulation control Supervised Learning Surrogate Gradient

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3rd International conference on Communication, control, and Intelligent systems, CCIS 2024

3rd International Conference on Communication, Control, and ...

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3rd International conference on Communication, control, and Intelligent systems, CCIS 2024

ISBN: (纸本)9798331528201

The proceedings contain 175 papers. The topics discussed include: an optimized inverse neural network control augmented with feedback PI controller for time-varying systems;image encryption using neural network based chaotic systems;investigating the impact of update frequency on PID controller performance in autonomous robotics;deep learning framework for constellation signal classification in underwater optical wireless communication systems;fuzzy based web automation for smart power supply controller;ensembled machine learning models for antenna optimization in wireless communication/ biomedical applications;vertically integrated substrate integrated cavity based filtering antenna;network intrusion detection system using autoencoders;and a comparative study of Wallace tree multiplier and binary multiplier performance.

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Lightweight Neural network for Highway Toll Vehicle Classification 36

Lightweight Neural Network for Highway Toll Vehicle Classifi...

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36th Chinese control and Decision conference (CCDC)

作者： Yang Jingkai Liu Weiyu ChangAn Univ Sch Elect & Control Engn Xian Shaanxi Peoples R China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387780;9798350387797

With the continuous development of computer vision technology, transportation systems are gradually approaching intelligence. This article proposes a lightweight neural network for highway toll vehicle classification, which can replace manual judgment of toll vehicle types. It is compared and validated with other object detection algorithms on the Beijing Hong Kong Macao Expressway dataset. The results show that the method proposed in this article has higher real-time performance and accuracy.

关键词： Freeway Deep Learning Image Recognition Vehicle Classification

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