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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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Incipient Sensor Fault Detection by Directly Monitoring Sliding Window Based Singular Values*

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IFAC-PapersOnLine 2022年第6期55卷 637-642页

作者： Wenqing Zhao Hao Luo Qiang Liu Hongquan Ji Nan Sheng College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao 266590 P. R. China Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 P. R. China

Timely and accurate detection of incipient faults is critical to guarantee the normal operation of industrial processes. Nowadays, complex systems are usually equipped with a large number of sensors, which may be vulnerable to faults due to harsh environments. Statistical process monitoring is commonly used for fault detection purpose. Nevertheless, traditional fault detection methods are not sensitive enough to incipient faults, leading to the occurrence of many missed alarms. In this paper, the incipient fault detection task is achieved by monitoring the changes of sample singular values within a sliding window. Two incipient sensor fault types are considered, i.e. the sensor constant bias fault and sensor precision degradation fault. In addition, the rationale behind this strategy is also theoretically analyzed. Finally, a numerical example and the continuous stirred tank reactor process demonstrate the effectiveness of the proposed method.

关键词： Incipient sensor fault fault detection constant bias precision degradation singular value

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Interpretable Soft Sensors using Extremely Randomized Trees and SHAP

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IFAC-PapersOnLine 2023年第2期56卷 8000-8005页

作者： Liang Cao Xiaolu Ji Yankai Cao Yupeng Li Lim C. Siang Jin Li Vijay Kumar Pediredla R. Bhushan Gopaluni Department of Chemical and Biological Engineering University of British Columbia Vancouver BC V6T 1Z3 Canada Department of Statistics University of British Columbia Vancouver BC V6T 1Z4 Canada School of Automation China University of Geosciences Wuhan 430074 China Department of Process Control Engineering Burnaby Refinery Burnaby BC V5C 1L7 Canada

Tree-based ensemble models are easy to implement and have been widely used in various fields. However, they have limitations in industrial process applications since the majority of tree-based ensemble models are prone to over-fitting. In addition, the internal structure of tree-based ensemble models is very complex and the output of the model is also difficult to explain, which makes its application in industrial soft sensors very challenging. The purpose of this work is to build accurate and interpretable soft sensors for industrial processes. First, to deal with overfitting, a robust tree-based ensemble model and extremely randomized trees are used to build accurate soft sensors. Then, to improve model interpretability, an interpretable machine learning algorithm, namely Shapely additive explanation, is used to infer the global and local contributions of each feature to the predictions. Finally, the effectiveness of the proposed algorithms is validated on real industrial fluid catalytic cracker unit data.

关键词： Interpretability SHAP Extremely Randomized Trees Soft Sensor

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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

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IEEE Transactions on Automatic control 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. ©1963-2012 IEEE.

关键词： Mixed-integer linear programming

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Asynchronous multi-rate systems of a transfer learning filtering algorithm using t-distribution

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2025年第5期42卷 947-954页

作者： Wang, Wei Zhao, Shun-Yi Zhang, Cheng-Xi Luan, Xiao-Li Liu, Fei Wu, Jin Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi214122 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology 999077 Hong Kong

This paper investigates a transfer learning filtering algorithm based on the t-distribution to address the problem of state estimation in asynchronous multi-rate sensor systems affected by outliers. By integrating full probability design and multi-scale system theory, we propose a novel transfer learning framework for asynchronous multi-rate systems. A multi-scale model is established to convert the asynchronous multi-rate system into a synchronous one. This design minimizes the Kullback-Leibler divergence between the predicted distribution in the source domain and the ideal distribution in the target domain, while allowing the sensor sampling rate ratio to be any positive integer. To account for outlier effects on state estimation, the source and target domains leverage the heavy-tailed properties of the t-distribution to model state and observation processes, with approximate estimation achieved through expectation-maximization and variational Bayesian methods. Simulation results on a planar position-velocity system demonstrate the superior performance of the proposed method. © 2025 South China University of Technology. All rights reserved.

关键词： Maximum likelihood estimation

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Resilient control of Networked control Systems With Hidden DoS Attacks and Unknown Observation Probability

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IEEE Transactions on control of Network Systems 2025年

作者： Wang, Yuan Yan, Huaicheng Park, Ju H. Zhang, Hao Shen, Hao East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China Weifang University College of Machinery and Automation Weifang261061 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of Tongji University Department of Control Science and Engineering Shanghai200092 China Anhui University of Technology School of Electrical and Information Engineering Ma'anshan243002 China

This paper systematically studies the problem of resilient controller design for networked control systems under imperfect communication environments. The denial of service (DoS) attacks in the case of an imperfect network are remodeled. The semi-Markov chain is introduced in this paper to provide a suitable description of random attacks. On this basis, the double-layer stochastic process is constructed, which consists of an attack sequence and an observation one. Second, since the attack modes are inaccessible, a controller is constructed based on the observation sequence by virtue of the emission probability to stabilize the system under random attacks. Due to the complexity of the network, the probability that an observer can accurately detect an attack when it occurs is considered unknown. Based on the introduced double-layer stochastic process, a mode-associated Lyapunov function is constructed to facilitate the stability analysis. Building on this foundation, sufficient conditions that can ensure the δ-error mean square stability of the closed-loop system are derived. In the end, a simulation of the unmanned ground vehicle is presented, showcasing the feasibility and validity of the proposed control scheme. © 2025 IEEE.

关键词： Stochastic systems

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Semi-Supervised Multiview Fuzzy Broad Learning

SSRN

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SSRN 2023年

作者： Xi, Chao Peng, Cheng Fan, Zizhu Liu, Qiang Wang, Hui Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang330013 China Department of Engineering King’s College London London4616 United Kingdom State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning110819 China

Semi-supervised learning models often rely on the restricted assumptions, and can easily suffer from corvariate shift or noise. Few studies have explored the use of fuzzy rule-based methods in the semi-supervised discipline. In this paper, we first build a semi-supervised fuzzy broad learning model called MT-FBLS to address these issues. Further, a trusted multiview semisupervised classification method, termed TMSSC, is proposed by integrating the MT-FBLS with a multiview fusion network to enhance the robustness of the model. Under the Mean-Teacher framework, MT-FBLS involves the Takagi-Sugeno-Kang(TSK) fuzzy model which can effectively deal with imprecision and uncertainty, and broad learning system(BLS) which has strong rule learning ability and high computational efficiency. TMSSC utilizes a trusted mechanism to blend multiple views, so as to enhance its learning ability in the semi-supervised *** on the benchmark datasets demonstrate that the proposed methods have better anti-noise ability, competitive classification accuracy, as well as fast running speed. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Hexapod Robot Gait Switching Based on Different Wild Terrains

Hexapod Robot Gait Switching Based on Different Wild Terrain...

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Data Driven control and Learning Systems (DDCLS)

作者： Shijie Fei Yiyang Chen Hongfeng Tao Hongtian Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

Hexapod robots have become an indispensable part of legged robots due to their excellent stability and traversability, and have recently become a hot research topic. In complex outdoor environments, the ability of the hexapod robot to switch its motion strategy according to the terrain is crucial for improving its mobility. This paper presents a terrain recognition network that enables our hexapod robot to switch its forward gait based on the different types of outdoor terrain. This method provides better stability for the hexapod robot than traditional single-gait walking through different terrains.

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Finite-time Consensus for Third-Order Multi-Agent Systems with Directed Communication Networks

Finite-time Consensus for Third-Order Multi-Agent Systems wi...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Xirui Zhang Zhi-Liang Zhao Tengfei Liu Zhong-Ping Jiang School of Mathematics and Statistics Shaanxi Normal University Xi’an P. R. China School of Electrical and Control Engineering North University of China Taiyuan P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang P. R. China Department of Electrical and Computer Engineering New York University Brooklyn NY USA

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper focuses on third-order multi-agent systems with directed communication networks. It is for the first time that the finite-time consensus problem is solved for third-order multi-agent systems under digraphs containing spanning trees. Two explicit-coefficient finite-time protocols, namely, edge-based and node-based, are proposed. First, we prove that asymptotic consensus is achievable with the reduced linear protocol. Then, we show that finite-time consensus is achievable with our explicit-coefficient finite-time protocols through homogeneous theory. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed protocols.

关键词： Protocols Network topology Numerical simulation Topology Communication networks Closed loop systems Multi-agent systems

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