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Stealthy False Data Injection Attacks in Multi-Channel Vehicular Communication: White-Box and Gray-Box Strategies

IEEE Transactions on Vehicular Technology 2025年

作者： Xu, Chongcong Zheng, Fangfang Guo, Ge Southwest Jiaotong University School of Transportation and Logistics Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

Understanding the vulnerabilities of vehicular networks from an attacker's perspective is crucial for identifying stealthy false data injection (FDI) attacks and developing robust defense methods. This paper investigates stealthy FDI attacks in multi-channel vehicular communication systems. We construct a multi-source state estimation system using a sequential Kalman filter to integrate and estimate redundant state information transmitted by preceding vehicles across multiple channels. To enhance the system's resilience against FDI attacks, we incorporate a Sage-Husa adaptive filter and a chi-squared detector, which dynamically estimate and detect channel noise based on estimation errors. Within this framework, we utilize real-time state information and channel transmission residual to determine the probability of an attack window occurring. The attack process is divided into three phases, during which critical noise disturbances are introduced to create attack windows, allowing the attack to progressively align with the target state. Based on this analysis, we develop an optimization model for attack generation with complete channel information (AG-with-CCI) to facilitate a white-box attack. Additionally, acknowledging that attackers may not have access to real-time information from all channels, we analyze the iterative equations of state estimation errors and residuals to establish the conditions under which stealthy FDI attacks can be successfully executed, enabling the attack generation with attacked channel information (AG-with-ACI) to achieve a gray-box attack. Numerical simulations demonstrate that both attack methods can evade current detection mechanisms and achieve their intended objectives. © 2025 IEEE.

关键词： Adaptive filtering

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Passivity-Based Online Reinforcement Learning for Real Time Model-Free Overhead Crane System control

Passivity-Based Online Reinforcement Learning for Real Time ...

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Chinese control and Decision Conference, CCDC

作者： Haoran Zhang Chunhui Zhao Jinliang Ding State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (纸本)9781665478977

In this paper, a novel model-free online Reinforcement Learning (RL) control method is proposed for the real-time overhead crane control problem. The crane control problem is first formulated as an optimal regulation problem with a user-specified objective function. Two neural-networks, namely Actor-Critic networks, are employed to approximate the objective function and the optimal control policy respectively. Then, an improved network updating rule with an additional passivity-based stabilization term is developed to remove the requirement of the initial stabilizing control policy. Unlike other crane control approaches, the proposed online RL algorithm does not rely on prior knowledge of the overhead crane mathematical model. Finally, simulation studies are carried out to demonstrate the effectiveness of the proposed method in the presence of system parameter variations when compared to LQR control.

关键词： Adaptation models Cranes System dynamics Reinforcement learning Linear programming Mathematical models Real-time systems

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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

Identification of Abnormal Operating Conditions in Fused Mag...

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Chinese automation Congress (CAC)

作者： Chao Xi Gaochang Wu Zizhu Fan Feng Xu Hui Wang Xiaokun Tang School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR).

关键词： Fuses Heuristic algorithms Furnaces Smelting Production Feature extraction Transformers Magnesium Classification algorithms Unsupervised learning

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Inducing Desired Equilibrium in Taxi Repositioning Problem with Adaptive Incentive Design

Inducing Desired Equilibrium in Taxi Repositioning Problem w...

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IEEE Conference on Decision and control

作者： Jianhui Li Youcheng Niu Shuang Li Yuzhe Li Jinming Xu Junfeng Wu College of Control Science and Engineering Zhejiang University Hangzhou China School of Data Science The Chinese University of Hong Kong (Shenzhen) Shenzhen China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

We study the problem of designing incentives to induce desired equilibrium in taxi repositioning problems. In this scenario, self-interested idle drivers will update their repositioning strategies with observed payoff. Meanwhile, the platform will adaptively design incentives to induce a better Nash equilibrium for global efficiency. We formulate the problem as a bi-level optimization problem where the incentive designer and idle drivers simultaneously update their decision variables. We prove that drivers' strategies will reach Nash equilibrium, and the incentive designer's objective function will reach optimality under Polyak Lojasiewicz (PL) condition. Furthermore, we derive a sufficient condition for the PL condition to hold for the upper-level objective function and lower-level agents' payoff function. Finally, we demonstrate the efficiency of the proposed method by numerical results.

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Data-Based Efficient Off-Policy Stabilizing Optimal control Algorithms for Discrete-Time Linear Systems via Damping Coefficients

arXiv

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arXiv 2024年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy Q-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Optimal control of a Gold Cyanide Leaching process Based on Reinforcement Learning

Optimal Control of a Gold Cyanide Leaching Process Based on ...

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第34届中国控制与决策会议

作者： Jun Zheng Runda Jia Fei Chu College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Engineering Research Center of Intelligent Control for Underground Space of the Ministry of Education School of Information and Control EngineeringChina University of Mining and Technology

ISBN: (纸本)9781665478977

Leaching by cyanide solutions in aerated alkaline slurry is the main process for gold extraction from *** this study,the model-based reinforcement learning(MBRL) is used to design an optimal controller for a gold cyanidation leaching process(GCLP) by automatically learning a control *** simulation experiments are performed on a first principle model to verify the effectiveness of the proposed *** results show that the MBRL method can learn an optimal control policy for the GCLP.

关键词： Optimal control Gold cyanide leaching process Probabilistic inference and learning for control Reinforcement learning

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Automated machine learning-assisted enhanced product carbon footprint tracking and analysis in refinery industry: A graph-based life cycle assessment framework

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Journal of Cleaner Production 2025年 514卷

作者： Zhang, Tingwei Peng, Xin Zhong, Weimin Shen, Feifei Ding, Jinliang State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai200237 China State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China

Product carbon footprint assessment and tracking in the refinery industry mainly rely on the expertise of carbon management professionals, which leads to high service costs and involves significant manual intervention, reducing the accuracy of carbon footprint analysis. Accordingly, a data-driven automated product carbon footprint analysis framework based on life cycle assessment is developed in this work. Specifically, an integrated material-energy-carbon multi-scale emission accounting model is established to quantify CO2 emissions accurately. Subsequently, considering that traditional carbon emission allocation methods are generally based on fixed assumptions or simplified proportional relationships, which fail to fully account for the dynamic variations in complex production processes, the allocation results lack accuracy and reliability. Additionally, these methods lack sufficient flexibility and are unable to effectively adjust to the actual carbon emission characteristics of different production stages and products. A data-driven contribution-based carbon emission allocation method is proposed from the carbon flow supply–demand balance perspective to improve allocation transparency. Moreover, a graph-based automated recognition framework of product carbon footprint chains is developed to achieve process operation automation and eliminate manual operations. The proposed models and methods are applied to a real refinery production site for a case study. The results show that the developed product carbon footprint analysis framework can highlight high-energy consumption and high-emission links in the production process and pay more attention to the environmental costs of high-value products. Moreover, CO2 can act as a carbon flow regulation hub, effectively managing emissions through the balance of carbon flow supply and demand. Light oil has a higher contribution rate to CO2 emissions than heavy oil. The carbon footprint of petroleum coke is 3.8677 tCO2eq/t, ma

关键词： Carbon footprint

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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

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Science China(Information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous Systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems process control Artificial neural networks control systems Approximation error

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