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Controllability of heterogeneous networked sampled-data systems

Science China(Information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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Unknown DDoS Attack Detection with Sliced Iterative Normalizing Flows Technique

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Computers, Materials & Continua 2025年第3期82卷 4881-4912页

作者： Chin-Shiuh Shieh Thanh-Lam Nguyen Thanh-Tuan Nguyen Mong-Fong Horng Department of Electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan Department of Electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services *** networks continue to expand and threats become more sophisticated,there is an urgent need for Intrusion Detection systems(IDS)capable of handling these challenges *** IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing *** paper presents a novel approach for detecting unknown Distributed Denial of Service(DDoS)attacks by integrating Sliced Iterative Normalizing Flows(SINF)into *** utilizes the Sliced Wasserstein distance to repeatedly modify probability distributions,enabling better management of high-dimensional data when there are only a few samples *** unique architecture of SINF ensures efficient density estimation and robust sample generation,enabling IDS to adapt dynamically to emerging threats without relying heavily on predefined signatures or extensive *** incorporating Open-Set Recognition(OSR)techniques,this method improves the system’s ability to detect both known and unknown attacks while maintaining high detection *** experimental evaluation on CICIDS2017 and CICDDoS2019 datasets demonstrates that the proposed system achieves an accuracy of 99.85%for known attacks and an F1 score of 99.99%after incremental learning for unknown *** results clearly demonstrate the system’s strong generalization capability across unseen attacks while maintaining the computational efficiency required for real-world deployment.

关键词： Distributed denial of service sliced iterative normalizing flows open-set recognition cybersecurity deep learning

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Kernel-Based State-Space Kriging for Predictive Control

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1263-1275页

作者： A.Daniel Carnerero Daniel R.Ramirez Daniel Limon Teodoro Alamo the Department of System Engineering and Automation University of SevilleSevilla 41020Spain the School of Engineering Tokyo Institute of TechnologyTokyo 152-8552Japan Department of System Engineering and Automation University of SevilleSevilla 41020Spain

In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging(K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control(NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller.

关键词： Data-driven methods model identification Kernel methods predictive control

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Autonomous embodied navigation task generation from natural language dialogues

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Science China(Information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Finite-time PID control for nonlinear nonaffine systems

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Science China(Information Sciences) 2024年第11期67卷 238-251页

作者： Zhiqing LIU Ronghu CHI Biao HUANG Zhongsheng HOU College of Mathematics and Physics Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao University of Science and Technology Department of Chemical and Materials Engineering University of Alberta School of Automation Qingdao University

This article proposes a finite-time proportional-integral-derivative(FT-PID) control method to fast stabilize the control system to achieve the desired performance within the predesignated time *** a considered nonaffined nonlinear system, we develop a new dynamic linearization approach to reformulate the system model as a linear data model(LDM) whose arguments are consistent with that used in the PID control law. Then, a projection algorithm is presented to estimate the unknown pseudo gradient vector of the LDM. Subsequently, an adaptive tuning algorithm is designed to update the three PID parameters by solving linear matrix inequalities in terms of the predesignated error precision and the finite-time instant. The finite-time convergence of the proposed FT-PID control system is shown mathematically, which guarantees a pre-specified error precision to be achieved within the predesignated finite-time instants. As a result, not only can the proposed FT-PID control save the control cost but it also improves the production efficiency. The simulation study verifies the results.

关键词： PID control finite-time control nonlinear nonaffine systems linear data model finite-time convergence

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

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Computers, Materials & Continua 2024年第2期78卷 2181-2205页

作者： Thanh-Lam Nguyen HaoKao Thanh-Tuan Nguyen Mong-Fong Horng Chin-Shiuh Shieh Department of Electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan Department of Electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

Since its inception,the Internet has been rapidly *** the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the *** applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the ***,malicious threats on the internet are on the rise as *** Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet *** scale and complexity of DDoS attacks are constantly *** Detection systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those *** addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant ***,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown *** attacks,which are not encountered during the system’s training,can lead to misclassification with significant *** this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional *** proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)***,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)*** success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering *** has yielded a novel method in the field of unknown attack *** further expands the trend of applying DL and M

关键词： Cybersecurity DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(CNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means CICIDS2017 CICDDoS2019

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Design of Lightweight Fully-Connected Network in Hardware Using Learning-Based Low-Rank Approximation and Quantization Techniques

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 149-163页

作者： Seo, Jeong-Yun Lee, Jong-Youn Park, Sung-Jun Lee, Harim School of Electronic Engineering Kumoh National Institute of Technology Korea Republic of Department of Semiconductor System Engineering Kumoh National Institute of Technology Korea Republic of

In this paper, we address the design of an AI hardware accelerator optimized for a lightweight fully-connected network. Techniques such as quantization, knowledge distillation, pruning, and low-rank approximation are utilized to reduce the number of weights, maintaining inference performance while minimizing memory requirements. We introduce a learning-based low-rank approximation that outperforms the original low rank approximation. In addition, the interrelationship between various compression techniques such as quantization, knowledge distillation, pruning, and low-rank approximation is analyzed to enhance the understanding of deep learning model compression. In order to use the decomposed weight matrices in hardware, we design a compressed fully-connected layer, utilized to construct a lightweight fully-connected network. The proposed hardware design is developed by using Verilog HDL and verified through RTL simulation. Copyright © The Korean Institute of Electrical Engineers.

关键词： Deep learning

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Hardware Implementation of Convolution Layer for Quantized Deep Neural Networks in Image Classification

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Transactions of the Korean Institute of Electrical Engineers 2025年第4期74卷 644-654页

作者： Lee, Jong-Youn Seo, Jeong-Yun Park, Sung-Jun Lee, Harim Lee, Yong-Hwan Department of Semiconductor System Engineering Kumoh National Institute of Technology Korea Republic of School of Electronic Engineering Kumoh National Institute of Technology Korea Republic of

In this paper, we introduce the design of an artificial intelligence (AI) hardware accelerator for a convolutional neural network (CNN) for classification tasks. The architecture of the AI hardware for a CNN model consists of two modules: a striding-padding (SP) module and a convolution layer module. For the classification task, fully-connected layers should follow the CNN model, and hence we also briefly explain the architecture of the fully-connected layer. In the architecture of a CNN model, the SP module manages how to access memories including feature maps and weights by considering the striding and padding operation of a convolution layer, and the convolution layer conducts convolution operation with feature maps and weights that are read from the memories. To verify the AI hardware design through RTL simulation, CNN models are trained in software using a MNIST dataset and a Fashion MNIST dataset. Then, post-training quantization and quantization-aware training, which are representative quantization techniques, are utilized to extract hardware-appropriate weights from the trained CNN models. The quantized weights are loaded to memories, and the validation data from the dataset used to train CNN models are fed to the designed CNN hardware. Finally, the inference results of the hardware are compared with that of the software-trained CNN models, which confirms the correctness of the hardware design. © The Korean Institute of Electrical Engineers.

关键词： Deep learning

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Constrained Networked Predictive Control for Nonlinear systems Using a High-Order Fully Actuated system Approach

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 478-480页

作者： Yi Huang Guo-Ping Liu Yi Yu Wenshan Hu the School of Electrical Engineering and Automation Wuhan University IEEE the Center for Control Science and Technology Southern University of Science and Technology the Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University

Dear Editor,In this letter, a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated (HOFA) systems with noises. The method can effectively deal with nonlinearities, constraints, and noises in the system, optimize the performance metric, and present an upper bound on the stable output of the system.

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