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arXiv 2025年

作者： Chen, Jialong Wang, Jimin Zhang, Ji-Feng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China School of Automation and Electrical Engineering Zhongyuan University of Technology Zheng Zhou450007 China

This paper proposes a new differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. Specifically, privacy noises are added to each agent’s state and tracking variable to prevent information leakage, and then perturbed states and tracking variables are transmitted to neighbors. We design two novel schemes of the iteration step-sizes and the sampling number for the algorithm. By using the sampling parameter-controlled subsampling method, both schemes enhance the differential privacy level, and achieve the finite cumulative privacy budget even over infinite iterations. The convergence rate of the algorithm is shown for both nonconvex with the Polyak-Lojasiewicz condition and strongly convex objectives: Scheme (S1) achieves the polynomial convergence rate, and Scheme (S2) achieves the exponential convergence rate. The trade-off between the privacy and the convergence rate is presented. The algorithm’s effectiveness and superior performance over the existing works are demonstrated through numerical examples of distributed training on benchmark datasets"MNIST" and"CIFAR-10". Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Safety-critical control with control Barrier Functions: A Hierarchical Optimization Framework

arXiv

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arXiv 2024年

作者： Xie, Junjun Hu, Liang Qin, Jiahu Yang, Jun Gao, Huijun The Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China The Department of Automation University of Science and Technology of China Hefei230085 China The Department of Aeronautical and Automotive Engineering Loughborough University LeicestershireLE11 3TU United Kingdom The Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

The control barrier function (CBF) has become a fundamental tool in safety-critical systems design since its invention. Typically, the quadratic optimization framework is employed to accommodate CBFs, control Lyapunov functions (CLFs), other constraints and nominal control design. However, the constrained optimization framework involves hyper-parameters to tradeoff different objectives and constraints, which, if not well-tuned beforehand, impact system performance and even lead to infeasibility. In this paper, we propose a hierarchical optimization framework that decomposes the multi-objective optimization problem into nested optimization sub-problems in a safety-first approach. The new framework addresses potential infeasibility on the premise of ensuring safety and performance as much as possible and applies easily in multi-certificate cases. With vivid visualization aids, we systematically analyze the advantages of our proposed method over existing QP-based ones in terms of safety, feasibility and convergence rates. Moreover, two numerical examples are provided that verify our analysis and show the superiority of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Motion Analysis and Experiment of Sheet Small Magnetron Soft Robot 8

Motion Analysis and Experiment of Sheet Small Magnetron Soft...

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8th International Conference on control Science and systems engineering, ICCSSE 2022

作者： Ma, Haoyun Zhang, Pan Wu, Jundong Wang, Yawu School of Automation China University of Geosciences Hubei Wuhan430 074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China

ISBN: (纸本)9781665484138

In the past, most robots used rigid structures. With the development of intelligent materials, more and more soft materials began to be used in the manufacture of robots. Magnetic-driven soft robot is one of its important branches. Because of the flexibility of magnetoelastic composite robots and the harmlessness of magnetic field to human body, a magnetically-controlled soft robot has broad application prospects in medical fields such as disease diagnosis and treatment in human body. A small lamellate magnetic soft robot is designed in this paper with the advantages of small volume, light weight, high deformation, fast and simple manufacture. And it can be driven without wire under the control of the magnetic field formed by the three-dimensional Helmholtz coil. The robot can walk and crawl on the surface of magnetically-controlled. The motion characteristics of the magnetic soft robot such as crawling step length and bending height are analyzed. The data are collected through experiments to analyze the relationship between the control signal and the motion characteristics. © 2022 IEEE.

关键词： Motion analysis

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Finite-time sliding-mode control for semi-Markov systems with delayed impulses

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Braininspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

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Triggered Mechanism for Strict Feedback systems Under Unknown control Directions

Triggered Mechanism for Strict Feedback Systems Under Unknow...

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Asian control Conference

作者： Guilong Liu Hongda Wu Yongliang Yang Wei Xie Weidong Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper presents an event-based asymptotic tracking control scheme designed for strict feedback systems with unknown control directions. The control directions directly determine how the controller acts on the system, thus impacting the system's stability and convergence speed. When the control directions are unknown, it presents a significant challenge in controller design. To tackle this issue, the Nussbaum-type gains are introduced. However, this method increases communication overhead. Hence, an event-triggered mechanism is introduced to alleviate this burden. Additionally, to achieve and maintain desired target states, satisfying control requirements and performance demands, this paper improves the event-triggered mechanism to achieve asymptotic tracking. Finally, the feasibility and effectiveness of the proposed design are validated through a simulation example.

关键词： Asymptotic stability Target tracking Stability criteria control systems Optimization Convergence

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Fixed-time adaptive parameter estimation for magnetostrictive actuated positioning system

Fixed-time adaptive parameter estimation for magnetostrictiv...

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第43届中国控制会议

作者： Shengbin Wu Jing Na Yingbo Huang Faxiang Zhang Xinkai Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Department of Electronic and Information Systems Shibaura Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An adaptive parameter estimation method that ensures fixed-time convergence is proposed for micro-nano positioning system driven by magnetostrictive actuator(i.e., magnetostrictive actuated positioning system). Firstly, the hysteresis nonlinearity of the magnetostrictive actuated positioning system is characterized using the generalized Prandtl–Ishlinskii(GPI) ***, the low-pass filter is introduced to design the adaptive estimation law driven by the estimation error, by which the unmeasured intermediate hysteresis variable can be avoided, and the estimation error converges to zero within a fixed time. Specifically,a modified error variable is designed to deal with the influence of the regression vector, thereby improving convergence performance and simplifying parameter adjustment. Finally, the effectiveness of the presented method is verified by simulation and experimental outcomes.

关键词： Adaptive parameter estimation Fixed-time convergence Magnetostrictive actuator Micro/nano positioning system

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Enabling technology and core theory of synthetic biology

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Science China(Life Sciences) 2023年第8期66卷 1742-1785页

作者： Xian-En Zhang Chenli Liu Junbiao Dai Yingjin Yuan Caixia Gao Yan Feng Bian Wu Ping Wei Chun You Xiaowo Wang Tong Si Faculty of Synthetic Biology Shenzhen Institute of Advanced TechnologyShenzhen 518055China Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Metabolism Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Tianjin Institute of Industrial Biotechnology Chinese Academy of SciencesTianjin 300308China Ministry of Education Key Laboratory of Bioinformatics Center for Synthetic and Systems BiologyBioinformatics DivisionBeijing National Research Center for Information Science and TechnologyDepartment of AutomationTsinghua UniversityBeijing 100084China

Synthetic biology provides a new paradigm for life science research(“build to learn”)and opens the future journey of biotechnology(“build to use”).Here,we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology,including synthesis and assembly of a genome,DNA storage,gene editing,molecular evolution and de novo design of function proteins,cell and gene circuit engineering,cell-free synthetic biology,artificial intelligence(AI)-aided synthetic biology,as well as *** also introduce the concept of quantitative synthetic biology,which is guiding synthetic biology towards increased accuracy and predictability or the real rational *** conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

关键词： synthetic biology quantitative synthetic biology genome synthesis and assembly DNA storage molecular evolution de novo design computer-aided design cell engineering gene circuit chassis cell artificial intelligent(AI) biofoundry

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Performance Analysis of PMSM Based on Improved Trapezoidal Halbach Magnet Arrays 17

Performance Analysis of PMSM Based on Improved Trapezoidal H...

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17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022

作者： Yu, Zihao Yan, Liang Zhou, Yuchen Liu, Jiatong He, Xinghua Aboelhassan, Ahmed Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Science and Technology on Aircraft Control Laboratory Beijing100191 China Beihang University Ningbo Institute of Technology Ningbo315800 China College of Engineering & Technology Electrical and Control Engineering Department Alexandria1029 Egypt University of Nottingham Ningbo China Key Laboratory of More Electric Aircraft Technology of Zhejiang Province Ningbo315100 China

ISBN: (纸本)9781665409841

In this paper, a novel trapezoidal Halbach permanent magnet array is introduced to lessen the output torque ripple of a surface-mounted Permanent Magnet Synchronous Motor (PMSM). Finite Element Method (FEM), core loss, output torque, and torque ripple are examined. The harmonic components of the trapezoidal Halbach magnet arrays are analyzed by the Fast Fourier Transform (FFT). Additionally, a traditional trapezoidal Halbach magnet array-based 2D finite element model of the PMSM is also developed. The findings demonstrate the improved trapezoidal Halbach magnet array's capability to reduce the PMSM's torque ripple by lowering the intensity of the third harmonic of the air-gap flux density. In contrast to the PMSM employing traditional trapezoidal Halbach magnet arrays, the PMSM with the introduced trapezoidal Halbach arrays has a lower torque ripple by 11.84 % and a higher torque by 18.03%, while maintaining similar core loss. © 2022 IEEE.

关键词： Finite element method

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Load Prediction Model of Gas Boiler Generator Set Based on CNN-LSTM 11th

Load Prediction Model of Gas Boiler Generator Set Based on...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Xu, Yan Wu, Min Hu, Jie Peng, Fu-Sheng Zhang, Wen Song, Wen-Shuo Li, Hui-Hang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789819647521

With the proposal of carbon peak and carbon neutrality targets and the construction of a new power system, improving energy efficiency and reducing carbon emissions have become the key to the sustainable development of industrial boiler generator sets. However, the current load scheduling of gas boiler power plants is mainly based on manual experience, which limits achieving more efficient and stable operation. Accurate short-term load forecasting can help dispatchers to make reasonable production schedules. Therefore, this paper proposes a hybrid load forecasting model for generator sets based on convolutional neural networks combined with long short-term memory networks. First, the Isolation Forest algorithm eliminates anomalies in the historical data set. Then, variables related to unit load are selected by combining mechanistic analysis with the Spearman algorithm. Considering the multiple operating conditions and high complexity of unit loads, the CNN-LSTM network is used to fully extract the spatial and temporal characteristics to build the load prediction model. Finally, experiments are conducted using actual production data, showing that the proposed method is effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric load dispatching

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model 42

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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42nd Chinese control Conference, CCC 2023

作者： Ao, Mengting Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789887581543

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Long short-term memory

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