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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Huang, Dehai Duan, Jihang Yang, Jianzhong Chen, Jihong Xu, Guangda National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819778867

The feed system is an important part of the CNC machine tool, and its performance directly affects the machining quality. Establishing an accurate model is the key to achieving feed system servo performance optimization and error compensation. In this paper, a fusion modeling method is proposed for the problems of low accuracy and difficulty in identifying stiffness and damping in the traditional lumped mass modeling method. The proposed method first establishes a rigid model without stiffness and damping, then uses a data-driven model to model the unmodeled dynamics, and finally connects the rigid model and the data-driven model in series. The method simplifies the lumped mass model, avoids the problem that the stiffness and damping are difficult to identify, and the fusion of the data-driven model significantly improves the prediction accuracy of the model. The experimental results show that the fused model has higher prediction accuracy for the frequency response characteristics and displacement of the feed system. © The Author(s) 2025.

关键词： Ball screws

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Swarm-Based Dynamic Coverage of Multi-ASV systems in the Presence of Measurement Noises

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7237-7250页

作者： Liu, Lu Jiao, Shijian Han, Bing Li, Tieshan Peng, Zhouhua Dalian Maritime University School of Marine Electrical Engineering Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China Shanghai Ship and Shipping Research Institute National Engineering Research Center of Ship & Shipping Control System Shanghai200135 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper is concerned with the dynamic coverage of multiple autonomous surface vehicles (ASVs) in the presence of static and dynamic obstacles, limited communication distance, and measurement noises. A modular control method including planning, guidance, and control is proposed. At the planning level, a swarm-based dynamic coverage strategy is developed to achieve the coverage of a given domain. Both the collision avoidance and connectivity preservation issues are solved by using artificial potential fields. At the kinematic level, novel adaptive line-of-sight guidance laws are presented based on cascade extended state observers (CESOs), such that accurate guidance signals are given by compensating for time-varying sideslip angle and suppressing measurement noises. At the kinetic level, two CESOs are developed to estimate the internal model uncertainties and external environmental disturbance, and antidisturbance kinetic control laws are designed subsequently. The proposed CESOs overcome the noises sensitivity problem of classical observers, and thus facilitate the practical implementation under measurement noises. The input-to-state stability of the closed-loop system is confirmed by the Lyapunov theory and cascade stability theory. Simulation and hardware-in-the-loop experiment results are provided to demonstrate the effectiveness of the proposed swarm-based dynamic coverage method. © 1967-2012 IEEE.

关键词： Velocity measurement

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Adaptive Artificial Potential Field Ship Motion Planning Based on DDPG

Adaptive Artificial Potential Field Ship Motion Planning Bas...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Huimin Liu, Chenguang He, Zhibo Zhang, Guoqing Han, Bing State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China Navigation College Dalian Maritime University Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China National Engineering Research Center of Ship & Shipping Control System Shanghai200134 China

ISBN: (纸本)9789819639601

The paper presents a ship motion planning method that adjusts the attractive and repulsive gain parameters of the artificial potential field (APF) algorithm using the deep deterministic policy gradient (DDPG) algorithm, to address the challenges of parameter design and limited adaptability of traditional APF algorithms. This approach leverages the speed advantage of the APF planning algorithm and the exploration capability of the DDPG reinforcement learning algorithm. By optimizing the parameters of APF with DDPG, the local optima problem can be alleviated in complex obstacle environments. Simulation results demonstrate that the proposed motion planning algorithm can generate reasonable trajectories and exhibit strong adaptability in complex obstacle scenarios. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Motion planning

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Resilient Cooperative control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of Environmental Science & engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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A Quasi-Parallel Resonant Switched Capacitor DC-DC Converter with Wide Input Voltage Range Regulation and Enhanced Dynamic Performances

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Qi, Jingjing Wu, Xuezhi Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Beijing Jiaotong University National Active Distribution Network Technology Research Center Beijing100044 China

Wide-range and high-efficiency DC-DC converters are in high demand for various applications, such as distributed systems, electric vehicles, and DC buildings. Resonant switched capacitor converters (ReSCC) can achieve soft switching, have small passive device capacity, and exhibit low voltage stress, but the voltage ratio is difficult to be regulated continuously. In this paper, a quasi-parallel resonant switched capacitor DC-DC converter is proposed to satisfy the design requirements, which incorporates a ReSCC with soft-switching and variable voltage ratio, as well as a Buck converter. In addition, a simplified optimal trajectory controller for ReSCC is investigated, which can be combined with PI controller of Buck converter to enhance the load transient of the proposed converter. Moreover, the input voltage feedforward control is implemented with the optimized feedforward coefficient to suppress the input voltage disturbance. The proposed converter can achieve high efficiency and rapid dynamic response over a wide conversion range. The correctness of the theoretical analyses is verified by a 36V∼60V-5V/20A prototype. © 2013 IEEE.

关键词： Capacitor bank

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A MPC-based Planner Applied on a Parallel Wheel-legged Robot for Obstacle Avoidance 25

A MPC-based Planner Applied on a Parallel Wheel-legged Robot...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and control engineering

作者： Fei Guo Huan Yu Wanhong Lin Shoukun Wang National Key Laboratory of Human Factors Engineering China Astronaut Research and Training Center Beijing China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400712647

A wheel-legged robot is equipped with Stewart parallel mechanism, constituting a reconfigurable robot which can change its wheelbase, robot body height, and achieve omnidirectional steering. The legged character effectively improves the terrain adaptability, which concerns our planning concentration. We introduced an optimization-based whole-body trajectory planning algorithm to navigate robot in rugged terrain. The planner combines terrain data and stability, allowing lower-level motion generator and controller to operate more efficiently. The Model Predictive control(MPC)-based method updates the footholds and CoG trajectories, which builds upon the support polygon constraints on optimization. The simulations of methodology working in several structure-obstacle scene demonstrated and compared the availability of approach.

关键词： Reconfigurable robot

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Low-Earth Orbital Lifetime Prediction Based on Parameter Sensitivity Analysis and Deep Learning

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Chen, Shun-Yi Yang, Zhen Wang, Hua Lu, Lin State Key Laboratory of Space System Operation and Control Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China China Astronaut Research and Training Center Beijing100094 China

This article proposes an intelligent prediction method for the orbital lifetime of resident space objects (RSOs) in low Earth orbit. This method is intended to satisfy the computational efficiency and accuracy requirements when predicting the large-scale RSOs. It helps to analyze the distribution and uncertainty of RSOs' lifetime. First, based on the semi-analytical orbit prediction model, a parameter sensitivity analysis method for the orbital lifetime is proposed, and the parameters that affect the orbital lifetime are investigated. Second, using a fully connected neural network, an orbital lifetime prediction model is established based on deep learning. The hyperparameters of the deep learning model are optimized using the black-winged kite algorithm. Finally, by combining the sensitivity analysis with the deep learning model, an intelligent prediction method is proposed for the rapid prediction of orbital lifetime. Simulation results show that the proposed intelligent prediction method accurately determines the orbital lifetime of RSOs and has higher prediction efficiency than the traditional prediction method. The research results provide an effective calculation and analysis tool for predicting the orbital lifetime of large-scale RSOs in the future. © 2025 IEEE.

关键词： Prediction models

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CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

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engineering 2015年第2期1卷 247-260页

作者： Jihong Chen Jianzhong Yang Huicheng Zhou Hua Xiang Zhihong Zhu Yesong Li Chen-Han Lee Guangda Xu National Numerical Control System Engineering Research Center Huazhong University of Science and Technology

Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

关键词： cyber-physical system （CPS） big data computer numerical control （CNC） machine tool electronic data of work processes instruction domain intelligent machining

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