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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

science China(Information sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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Distributed coordination control of multi-agent systems under intermittent sampling and communication: a comprehensive survey

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science China(Information sciences) 2025年第5期68卷 289-331页

作者： Xiaohua GE Qing-Long HAN Xian-Ming ZHANG Derui DING Boda NING School of Engineering Swinburne University of Technology Department of Control Science and Engineering University of Shanghai for Science and Technology Department of Electrical and Electronic Engineering Auckland University of Technology

Sampling and communication are both crucial for coordination in multi-agent systems(MASs), with sampling capturing raw data from the environment for control decision making, and communication ensuring the data is shared effectively for synchronized and informed control decisions across agents. However, practical MASs often operate in environments where continuous and synchronous data samplings and exchanges are impractical, necessitating strategies that can handle intermittent sampling and communication constraints. This paper provides a comprehensive survey of recent advances in distributed coordination control of MASs under intermittent sampling and communication, focusing on both foundational principles and state-of-the-art techniques. After introducing fundamentals, such as communication topologies,agent dynamics, control laws, and typical coordination objectives, the distinctions between sampling and communication are elaborated, exploring deterministic versus random, synchronous versus asynchronous, and instantaneous versus sequential scenarios. A detailed review of emerging trends and techniques is then presented, covering time-triggered, event-triggered,communication-protocol-based, and denial-of-service-resilient coordination control. These techniques are analyzed across various attack models, including those based on data loss, sampled data, time constraints, and topology switching. By synthesizing these developments, this survey aims to equip researchers and practitioners with a clearer understanding of current challenges and methodologies, concluding with insights into promising future directions.

关键词： multi-agent systems distributed coordination control sampled-data control event-triggered control resilient control denial-of-service attacks communication protocols event-triggered mechanisms

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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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Fast fixed-time three-dimensional terminal guidance with non-concave trajectory constraint

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Chinese Journal of Aeronautics 2024年第3期37卷 208-218页

作者： Youmin GONG Yanning GUO Yueyong LYU Dongyu LI Guangfu MA Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

Focusing on the non-concave trajectory constraint,a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing,planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this ***,a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance,whose estimation error can converge to zero after a time independent of the initial ***,a sliding surface ensuring fixed-time convergence is *** sliding surface can guarantee that the vehicle achieves a non-concave trajectory,which is better for avoiding collision and maintaining the visibility of the landing site or docking ***,the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed,which is continuous and chatter *** last,three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.

关键词： Three-dimensional Terminal guidance Non-concave trajectory constraint Fast fixed-time control Sliding mode control

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Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1060-1062页

作者： Hao Zhang Yan Li Zhuping Wang Yi Ding Huaicheng Yan the Department of Control Science and Engineering Tongji UniversityShanghai 200092China the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and EngineeringEast China University of Science and TechnologyShanghai 200237China

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is... 详细信息

关键词： policy gradient Optimal

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ComMatch: A semi-supervised learning classification algorithm based on model calibration

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Journal of Intelligent and Fuzzy Systems 2025年第6期48卷 811-822页

作者： Li, Ye Zhou, Jingkang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China

Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image classification tasks. In this study, we propose ComMatch, a simpler and more effective algorithm that combines negative learning, dynamic thresholding, and predictive stability discriminations into the consistency regularization approach. The introduction of negative learning is to help facilitate training by selecting negative pseudo-labels during stages when the network has low confidence. And ComMatch filters positive and negative pseudo-labels more accurately as training progresses by dynamic thresholds. Since high confidence does not always mean high accuracy due to network calibration issues, we also introduce network predictive stability, which filters out samples by comparing the standard deviation of the network output with a set threshold, thus largely reducing the influence of noise in the training process. ComMatch significantly outperforms existing algorithms over several datasets, especially when there is less labeled data available. For example, ComMatch achieves 1.82% and 3.6% error rate reduction over FlexMatch and FixMatch on CIFAR-10 with 40 labels respectively. And with 4000 labeled samples, ComMatch achieves 0.54% and 2.65% lower error rates than FixMatch and MixMatch, respectively. © 2025 – IOS Press. All rights reserved.

关键词： Semi-supervised learning

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Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1514-1524页

作者： Ce Yang Weiqing Sun Jiannan Yang Dong Han Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Economics and Business Administration Chongqing UniversityChongqing 400044China

With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic *** the uncertainty of renewable energy generation,based on the distributionally robust optimization method,a two-stage economic dispatch model is proposed to minimize the total operation *** this paper,it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set,which is established by utilizing the first-order moment information of available historical ***,the theory of conditional value-at-risk is introduced to transform the model into a tractable model,which we call robust counterpart *** on the stochastic dual dynamic programming method,an improved iterative algorithm is proposed to solve the robust counterpart ***,the convergence optimum can be obtained by the improved iterative algorithm,which performs a forward pass and backward pass repeatedly in each iterative ***,by comparing with other methods,the results on the modified IEEE 6-bus,118-bus,and 300-bus system show the effectiveness and advantages of the proposed model and method.

关键词： Energy and reserve co-dispatch distributionally robust conditional value-at-risk stochastic dual dynamic programming

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Coalition formation problem: a capability-centric analysis and general model

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science China(Information sciences) 2024年第11期67卷 180-193页

作者： Jie CHEN Miao GUO Bin XIN Qing WANG Shengyu LU Yipeng WANG Yulong DING School of Automation Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering Tongji University

Coalition formation(CF) refers to reasonably organizing robots and/or humans to form coalitions that can satisfy mission requirements, attracting more and more attention in many fields such as multirobot collaboration and human-robot collaboration. However, the analysis on CF problems remains *** provide a valuable study reference for researchers interested in CF, this paper proposed a capabilitycentric analysis of the CF problem. The key problem elements of CF are firstly extracted by referencing the concepts of the 5W1H method. That is, objects(who) form coalitions(what) to accomplish missions(why) by aggregating capabilities(how) in a specific environment(where-when). Then, a multi-view analysis of these elements and their correlation in terms of capabilities is proposed through various logic diagrams, structure charts, etc. Finally, to facilitate a deeper understanding of capability-centric CF, a general mathematical model is constructed, demonstrating how the different concepts discussed in this analysis contribute to the overall model.

关键词： coalition formation capability aggregation capability metric mission requirement environmental effect

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2233-2247页

作者： Hongyu Ding Yuanze Tang Qing Wu Bo Wang Chunlin Chen Zhi Wang the Department of Control Science and Intelligence Engineering School of Management and EngineeringNanjing UniversityNanjing 210093China the Department of Power Engineering and Process Machinery School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237China IEEE

Goal-conditioned reinforcement learning(RL)is an interesting extension of the traditional RL framework,where the dynamic environment and reward sparsity can cause conventional learning algorithms to *** shaping is a practical approach to improving sample efficiency by embedding human domain knowledge into the learning *** reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution,which may fail to provide sufficient information about the ever-changing environment with high *** paper proposes a novel magnetic field-based reward shaping(MFRS)method for goal-conditioned RL tasks with dynamic target and *** by the physical properties of magnets,we consider the target and obstacles as permanent magnets and establish the reward function according to the intensity values of the magnetic field generated by these *** nonlinear and anisotropic distribution of the magnetic field intensity can provide more accessible and conducive information about the optimization landscape,thus introducing a more sophisticated magnetic reward compared to the distance-based ***,we transform our magnetic reward to the form of potential-based reward shaping by learning a secondary potential function concurrently to ensure the optimal policy invariance of our *** results in both simulated and real-world robotic manipulation tasks demonstrate that MFRS outperforms relevant existing methods and effectively improves the sample efficiency of RL algorithms in goal-conditioned tasks with various dynamics of the target and obstacles.

关键词： Dynamic environments goal-conditioned reinforcement learning magnetic field reward shaping

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