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Power Switching Based on Trajectory Planning and Sliding Mode control for Solid Oxide Fuel Cell Systems

Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1968-1979页

作者： Zhen Wang Guoqiang Liu Xingbo Liu Jie Wang Zhiyang Jin Xiaowei Fu Zhuo Wang Bing Jin Zhonghua Deng Xi Li Key Laboratory of Imaging Processing and Intelligent Control of Ministry of Education School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Research Institute of Huazhong University of Science and Technology in Shenzhen ShenzhenChina Hubei Huazhong Electric Power Technology Development Co. Ltd.WuhanChina School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhanChinaand he is also with the Wuhan Huamao Automation Co.Ltd.WuhanChina

To improve the safety of the solid oxide fuel cell(SOFC)systems and avoid the generation of large amounts of pollutants during power switching,this paper designs a power switching strategy based on trajectory planning and sliding mode control(TP-SMC).The design elements of the power switching strategy are proposed through simulation analysis at ***,based on the gas transmission delay time and the change of gas flow obtained from testing,trajectory planning(TP)is *** with other power switching strategies,it has been proven that the power switching strategy based on TP has significantly better control ***-thermore,considering the shortcomings and problems of TP in practical application,this paper introduces sliding mode control(SMC)on the basis of TP to improve the power switching *** final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay *** with TP,TP-SMC can ensure that under uncertain conditions,the SOFC system does not experience fuel starvation and temperature exceeding limit during power ***,the NOx emissions are also within the normal and acceptable *** paper can guide the power switching process of the actual SOFC sys-tems to avoid safety issues and excessive generation of NOx,which is very helpful for improving the performance and ser-vice lifeof the SOFCsystems.

关键词： Solid oxide fuel cell(SOFC)system power switching safety NOx emission trajectory planning sliding mode control

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Machine learning for predicting fatigue properties of additively manufactured materials

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Chinese Journal of Aeronautics 2024年第4期37卷 1-22页

作者： Min YI Ming XUE Peihong CONG Yang SONG Haiyang ZHANG Lingfeng WANG Liucheng ZHOU Yinghong LI Wanlin GUO State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China Institute for Frontier Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Shenyang Engine Research Institute Shenyang 110015China Liaoning Key Laboratory of Impact Dynamics on Aero Engine Shenyang 110015China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China

Fatigue properties of materials by Additive Manufacturing(AM) depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluation inevitably requires these factors combined as many as possible, thus resulting in low efficiency and high cost. In recent years, their assessment by leveraging the power of Machine Learning(ML) has gained increasing attentions. A comprehensive overview on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials, as well as their dependence on AM processing and post-processing parameters such as laser power, scanning speed, layer height, hatch distance, built direction, post-heat temperature,etc., were presented. A few attempts in employing Feedforward Neural Network(FNN), Convolutional Neural Network(CNN), Adaptive Network-Based Fuzzy Inference System(ANFIS), Support Vector Machine(SVM) and Random Forest(RF) to predict fatigue life and RF to predict fatigue crack growth rate are summarized. The ML models for predicting AM materials' fatigue properties are found intrinsically similar to the commonly used ones, but are modified to involve AM features. Finally, an outlook for challenges(i.e., small dataset, multifarious features,overfitting, low interpretability, and unable extension from AM material data to structure life) and potential solutions for the ML prediction of AM materials' fatigue properties is provided.

关键词： Additive manufacturing Machine learning Fatigue life Fatigue crack growth rate Prediction

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An active high-static-low-dynamic-stiffness vibration isolator with adjustable buckling beams:theory and experiment

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Applied Mathematics and Mechanics(English Edition) 2024年第3期45卷 425-440页

作者： Kefan XU Muqing NIU Yewei ZHANG Liqun CHEN School of Science Harbin Institute of TechnologyShenzhen 518055Guangdong ProvinceChina College of Aerospace Engineering Shenyang Aerospace UniversityShenyang 110136China Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Frontier Science Center of MechanoinformaticsShanghai Institute of Applied Mathematics and MechanicsSchool of Mechanics and Engineering ScienceShanghai UniversityShanghai 200444China Shanghai Institute of Aircraft Mechanics and Control Shanghai 200092China

High-static-low-dynamic-stiffness(HSLDS)vibration isolators with buckling beams have been widely used to isolate external *** active adjustable device composed of proportion integration(PI)active controllers and piezoelectric actuators is proposed for improving the negative stiffness stroke of buckling beams.A nonlinear output frequency response function is used to analyze the effect of the vibration *** prototype of the active HSLDS device is built,and the verification experiment is *** results show that compared with the traditional HSLDS vibration isolator,the active HSLDS device can broaden the isolation frequency bandwidth,and effectively reduce the resonant amplitude by adjusting the active control *** maximum vibration reduction rate of the active HSLDS vibration isolator can attain 89.9%,and the resonant frequency can be reduced from 31.08 Hz to 13.28 ***,this paper devotes to providing a new design scheme for enhanced HSLDS vibration isolators.

关键词： active control high-static-low-dynamic-stiffness(HSLDS) vibration isola-tor dynamic analysis

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Autonomous Navigation Algorithm for GEO Satellites During Hall Thruster Trajectory control in All-Time

Autonomous Navigation Algorithm for GEO Satellites During Ha...

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Chinese Automation Congress (CAC)

作者： Xiao Liu Sihong Gao Jupin Liang Kun Xiang Zhengyong Guo Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology Institute Shanghai CHINA

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper mainly studies the autonomous navigation and orbit propagation algorithm of the Hall electric propulsion as the thrust control device for geostationary orbit satellites during the thrust control period. Compared with low-altitude satellites, high-altitude satellites generally have relatively poor GNSS velocity measurement accuracy during thrust control, as well as small thrust generated by the Hall electric propulsion, and the tachometer on the large-inertia, large-mass satellite cannot accurately measure the thrust control acceleration, resulting in a decline in the original autonomous navigation accuracy during thrust control. This paper proposes a joint autonomous navigation algorithm for the Hall electric propulsion during thrust control using the EKF and local optimal filtering. The EKF filter suppresses the measurement white noise of the GNSS positioning data, and then the local optimal filter estimates the Hall electric propulsion acceleration and feeds it back to the EKF filter algorithm in real time to improve the navigation accuracy of the Hall electric propulsion during operation. Simulation results show that the joint autonomous navigation algorithm based on the above filtering method can accurately estimate the size of the Hall electric propulsion acceleration, and can effectively improve the autonomous navigation accuracy during continuous small thrust control.

关键词： Global navigation satellite system Satellites Accuracy Filtering Electric variables measurement Propulsion Filtering algorithms Extraterrestrial measurements Velocity measurement Autonomous robots

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Broadband and High-Power Sub-terahertz Gyro-TWTs and Ultrashort Electromagnetic Pulse Generation 5th

Broadband and High-Power Sub-terahertz Gyro-TWTs and Ultrash...

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5th China and International Young Scientist Terahertz Conference, YTHZ 2024

作者： Liu, Guo Jiang, Wei Yao, Yelei Wang, Weijie Cao, Yingjian Wang, Yu Wu, Zewei Pu, Youlei Wang, Jianxun Luo, Yong University of Electronic Science and Technology of China Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China

ISBN: (纸本)9789819739127

In this paper, a short demonstration about the recent progress of our high-power broadband gyrotron travelling wave tube (gyro-TWT) amplifiers in the sub-terahertz band (W and G-band) are respectively reported. Hot test of the W- and G-band gyro-TWTs had respectively achieved a pulsed/averaged output power of over 120 kW/20 kW and 35 kW/2.1 kW within a 10 GHz bandwidth. Based on the fabricated sub-terahertz gyro-TWTs, a scheme to generate high power, ultrashort electromagnetic pulses based on the chirped signal compressor is also presented, which had been verified in the micro- and millimeter wave band. The presented sub-terahertz high-power sources and ultrashort pulses have potential applications in the high-resolution imaging radar, deep space communication, bio-medicine, material modification and catalysis, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gyroscopes

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Application of Computer Vision technology in Collaborative control of the "Zhurong" Mars Rover 18th

Application of Computer Vision Technology in Collaborative C...

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18th Chinese Conference on Image and Graphics technology and Application Conference, IGTA 2023

作者： Wang, Jia Hu, Guolin Li, Dafei Wang, Saijin Han, Shaojin Li, Xin Liu, Xiaohui Cheng, Ziqing Zhang, Hui Huang, Zhao He, Ximing Wang, Xiaoxue Beijing Aerospace Control Center Beijing100094 China Key Laboratory of Science and Technology on Aerospace Flight Dynamics Beijing100094 China

ISBN: (纸本)9789819975488

The "Zhurong" Mars rover successfully landed on the surface of Mars on May 22, 2021, and began its inspection and exploration. Currently, it has traveled 1921 m and achieved a series of important scientific discoveries. Due to objective difficulties such as harsh terrain conditions, insufficient energy, and limited communication, the "Zhurong" Mars rover adopts a collaborative control mode of "semi intelligent on board autonomous + ground teleoperation". Computer vision technology is the key core technology supporting the collaborative control of the rover. In the enter descent landing stage and Mars surface roving, computer vision technology has broken through key technologies, such as rapid localization of the lander, terrain analysis of landing areas, three-dimensional terrain reconstruction, terrain classification, navigation and positioning, path planning, and simulation verification, solved the problem of long-term efficient mobile technology of the rover, and significantly expanded the detection range of the Mars rover, robustly guaranteeing the efficient and stable inspection and long-term survival of the Mars rover. This paper elaborates on the aforementioned technologies and their special on-orbit applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Airfoil Aerodynamic Force Prediction Based on Graph Neural Networks 43

Airfoil Aerodynamic Force Prediction Based on Graph Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Guoqing Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Aerodynamic force prediction is an important application of deep learning in the field of fluid mechanics. In this paper, a light graph neural network model with residual connections named LRGCN is designed to speed the computational process of the aerodynamic prediction. Comparing with other graph convolutional networks, the LRGCN reduces redundant operations and introduces skip connections, overcomes the limitations of traditional machine learning methods that require geometric airfoil parameters. By using CFD simulation, a dataset of aerodynamic forces for airfoil profiles was generated, where the nodes and edges of the flow field grids are abstracted to build a graph structure for aerodynamic force prediction. This study takes variations of the airfoil shape and pitch angle into consideration, enabling the prediction of lift and drag to be fast. In the numerical experiments, the LRGCN achieves the result that normalized test set loss is around 10-3 and the R2 is higher than 90%, which exhibits stronger robustness and higher accuracy than three other current convolutional models. And the LRGCN runs 60 times faster than the normal CFD method for the same prediction task, which significantly reduces time costs. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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Solar evaporation for simultaneous oil-water separation and electricity generation with Janus wood-based absorbers

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Frontiers of Environmental science & Engineering 2024年第2期18卷 13-23页

作者： Yue Yang Ze Fu Qi Zhang Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological EngineeringNanjing University of Science&TechnologyNanjing 210094China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesKey Laboratory of Intelligent Optical Sensing and Manipulation(Ministry of Education)Jiangsu Key Laboratory of Artificial Functional MaterialsNanjing UniversityNanjing 210093China

Oily wastewater from ocean oil spills endangers marine ecosystems and human health. Therefore, developing an effective and sustainable solution for separating oil-water mixtures is urgent. Interfacial solar photothermal evaporation is a promising approach for the complete separation of two-phase mixtures using only solar energy. Herein, we report a carbonized wood-based absorber with Janus structure of comprising a hydrophobic top-layer and an oleophobic bottom-layer for simultaneous solar-driven oil-water separation and electricity generation. Under sunlight irradiation, the rapid evaporation of seawater will induce a separation of oil-water mixtures, and cause a high salt concentration region underlying the interface, while the bottom “bulk water” maintains in a low salt concentration, thus forming a salinity gradient. Electricity can be generated by salinity gradient power. Therefore, oil-water separation efficiency of > 99% and derived extra electricity power of ~0.1 W/m2 is achieved under solar radiation, demonstrating the feasibility of oil-water separation and electricity production synchronously directly using solar energy. This work provides a green and cost-effective path for the separation of oil-water mixtures.

关键词： Oily wastewater Carbonized wood Salinity gradient Electricity generation Solar irradiation

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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intelligent Flight control for Cruise Vehicle Based on Backstepping Design and Reinforcement Learning

Intelligent Flight Control for Cruise Vehicle Based on Backs...

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2023 China Automation Congress, CAC 2023

作者： Liu, Xiaodong Jia, Chenhui Yang, Qingjun Wu, Tiancai Zhang, Yu Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China Beijing Aerospace Automatic Control Institute Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350303759

Aiming at velocity/height tracking control and attitude stabilization for the longitudinal flight of cruise vehicle, the research of intelligent flight control method is carried out by combining the backstepping control theory and the deep reinforcement learning method. Firstly, the longitudinal flight control system of the vehicle is decomposed into velocity and height subsystems, and the control law is designed based on backstepping control theory. Then, aiming at the problems that the selection of the multiple control gains will directly affect the control performance of the closed-loop system and the adjustment process of control gains is cumbersome, the twin delayed deep deterministic policy gradient algorithm (TD3) will be adopted to train the cruise vehicle, so that it can determine the control gains online according to the current flight states. The numerical simulation results show that, the proposed intelligent flight control method can guarantee the high-precision tracking with respect to height and velocity commands, and also realize the attitude stability of cruise vehicle. © 2023 IEEE.

关键词： Reinforcement learning

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