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Design and Performance Analysis of HMDV Dynamic Inertial Suspension Based on Active Disturbance Rejection control

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1485-1506页

作者： Xiaofeng Yang Wei Wang Yujie Shen Changning Liu Tianyi Zhang School of Automotive and Traffic Engineering Jiangsu UniversityZhenjiang212013China Automotive Engineering Research Institute Jiangsu UniversityZhenjiang212013China Chongqing Key Laboratory of Urban Rail Transit System Integration and Control Chongqing400074China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan universityChangsha410082China

This paper addresses the impact of vertical vibration negative effects,unbalanced radial forces generated by the static eccentricity of the hub motor,and road excitation on the suspension performance of Hub Motor Driven Vehicle(HMDV).A dynamic inertial suspension based on Active Disturbance Rejection control(ADRC)is proposed,combining the vertical dynamic characteristics of dynamic inertial suspension with the features of ADRC,which distinguishes between internal and external disturbances and arranges the transition ***,a simulation model of the static eccentricity of the hub motor is established to simulate the unbalanced radial electromagnetic force generated under static eccentricity.A quarter-vehicle model of an HMDV with a controllable dynamic inertial suspension is then ***,the passive suspension model is studied under different grades of road excitation,and the impact mechanism of suspension performance at speeds of 0–20 m/s is ***,the three main components within the ADRC controller are designed for the second-order controlled system,and optimization algorithms are used to optimize its internal ***,the performance of the traditional passive suspension,the PID-based controllable dynamic inertial suspension,and the ADRC-based controllable dynamic inertial suspension are analyzed under different road *** results show that,under sinusoidal road input,the ADRC-based controllable dynamic inertial suspension exhibits a 52.3%reduction in the low-frequency resonance peak in the vehicle body acceleration gain diagram compared to the traditional passive suspension,with significant performance optimization in the high-frequency *** random road input,the ADRC-based controllable dynamic inertial suspension achieves a 29.53%reduction in the root mean square value of vehicle body acceleration and a 14.87%reduction in dynamic tire *** indicates that the designed controllable dynamic inertia

关键词： HMDV static eccentricity ADRC dynamic inertial suspension road excitation

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ADAPTIVE BOUNDARY control VIBRATION SUPPRESSION OF FLEXIBLE MANIPULATOR BASED ON IMPROVED RBF NEURAL NETWORK

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UPB Scientific Bulletin, Series D: Mechanical Engineering 2024年第2期86卷 3-18页

作者： Zheng, Qingchun Wei, Zhiyong Zhu, Peihao Ma, Wenpeng Deng, Jieyong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China China School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Jiangxi Technical College of Manufacturing Jiangxi Nanchang330095 China

With lighter weight, lower power consumption, and higher flexibility, flexible manipulators have been widely used in industrial fields. However, it is easy to produce elastic deformation, resulting in vibration. In this paper, an improved radial basis function (RBF) neural network (NN) for adaptive boundary control is proposed. The partial differential equation (PDE) model of the flexible manipulator is established, and the asymptotic stability of the system is proved by the first Lyapunov method. Finally, simulation results show that the proposed control method can eliminate the low-amplitude angular fluctuation and the high-amplitude angular velocity fluctuation. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Flexible manipulators

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

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IEEE Transactions on Intelligent Transportation Systems 2024年第5期25卷 4552-4564页

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng Wuhan University of Technology Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan430063 China State Key Laboratory of Maritime Technology and Safety Wuhan430063 China National Taiwan University of Science and Technology Graduate Institute of Automation and Control and the Advanced Manufacturing Research Center Taipei10617 Taiwan Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. © 2000-2011 IEEE.

关键词： Real time systems

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Multi-stream fusion network for continuous gesture recognition based on sEMG

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 374-383页

作者： Li, Jun Zou, Chunlong Tang, Dalai Sun, Ying Fan, Hanwen Li, Boao Tang, Xinjie Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Inner Mongolia University of Finance and Economics Hohhot China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China

Dynamic gesture recognition is an essential step in human-computer interaction. However, dynamic gesture recognition based on Surface Electromyography (sEMG) signals faces issues such as incomplete feature extraction and incomplete recognition of temporal information between continuous gestures. Therefore, this paper proposes a multi-stream fusion network (MSK-LCNN) for dynamic gesture recognition to improve accuracy. We combine CNN and LSTM models into a unified framework which extracts spatio-temporal information both globally and in depth, and combines feature fusion to retain essential information. This framework uses an attention mechanism (SKNet) to learn more intricate feature information. The gesture recognition accuracy of this method on our data set is 95.23%, and on the NinaPro DB1 data set, it is 91.45%, which outperforms other similar networks in recent years. Applying this algorithm to prosthetic hand control has achieved flexible and stable control of the prosthetic hand. © 2024 Inderscience Enterprises Ltd.

关键词： Gesture recognition

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Research on Spherical Self-rotating Polishing technology 8

Research on Spherical Self-rotating Polishing Technology

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2022 8th International Forum on manufacturing technology and Engineering Materials, IFEMMT 2022

作者： Zhu, Tongbo Cai, Fan Provincial Key Laboratory of Industrial Robot Measurement and Control Rapid Die Manufacturing Minnan University of Science and Technology Fujian Quanzhou362700 China

Polishing technology is widely used in the polishing process of aspheric optical elements, complex curved surface molds, and so on because of its high machining accuracy, good flexibility, and other advantages. Based on the Preston equation, this paper deduces the material removal function of spherical self-rotating polishing by applying the kinematics principle, obtains the removal function with approximate Gaussian distribution by using computer simulation method, conducts processing technology experiments through the spherical self-rotating polishing device, analyzes the influence of polishing pressure and polishing speed on the polishing removal characteristics of ceramic materials by simulation, and verifies the stability of the removal function through experiments. The results show that the removal function graph is Gaussian distribution, which is conducive to the surface shape convergence of airbag machining. The influence of process parameters on material removal rate is in good agreement with the theory and practice and has a good linear relationship. It is a controllable and deterministic processing mode. © Published under licence by IOP Publishing Ltd.

关键词： Gaussian distribution

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PINN-based neural network for photoelastic stress recovery

PINN-based neural network for photoelastic stress recovery

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2023 advanced Fiber Laser Conference, AFL 2023

作者： Lin, Peng Tao, Bo Wang, Yan Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781510677661

Optical measurement techniques have emerged as a promising alternative to traditional invasive methods for stress recovery. Recent developments in artificial intelligence have enabled researchers to harness Physics-Informed Neural Networks (PINNs) for solving complex physics-based problems, including stress field recovery from photoelastic fringes. This paper explores the potential of PINNs for non-invasive stress analysis, presenting a novel methodology that integrates photoelastic fringes and physical stress distribution constraints within a deep learning framework. Our results demonstrate the model's robustness in handling diverse stress patterns and its ability to accurately recover stress distributions even when trained on significantly reduced datasets. A comparative analysis shows our PINN-based model's performance surpassing or closely matching existing methods like PhotoelastNet, underscoring its competitive advantage in stress recovery. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Stress concentration

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Optimised design of cross-shaft parameters based on response surface optimisation model with MOGA

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International Journal of Wireless and Mobile Computing 2024年第2期26卷 198-206页

作者： Xiong, Sijie Xie, Yuanmin Zou, Chunlong Mao, Yanfeng Cao, Yongcheng 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 Precision Manufacturing Research Institute Wuhan University of Science and Technology Hubei Wuhan China Hubei University of Automotive Technology Hubei Shiyan China Technology Research and Development Hubei Jingmen Wusan Machinery Equipment Manufacturing Co. Ltd. Jingmen Hubei China

Cross-shaft is the core component of the cross-type universal coupling and has a vital transmission function. This paper proposes Sparse Grid and the Kriging interpolation to construct a response surface model to solve the problem of long design cycles, low reliability and high susceptibility to cross-shaft fatigue deformation. The critical dimensions of the cross-shaft are used as design variables, and the maximum equivalent force and deformation are reduced as the optimisation objective. Then experimental points are obtained by Sparse Grid Initialisation and then the response surface model is obtained with high accuracy by Kriging interpolation, and finally, the optimised design of the cross-shaft is completed using MOGA in this paper. Compared with the original structural solution, the maximum deformation of the cross-shaft is reduced by 0.4717 mm (22.35%), the maximum equivalent force is reduced by 130.35 Mpa (17.21%) and the mass increased by only 4.17%. © 2024 Inderscience Enterprises Ltd.

关键词： Multiobjective optimization

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Object Detection with Depth Information in Road Scenes 8th

Object Detection with Depth Information in Road Scenes

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Liu, Ruowang Chen, Xinbo Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9789819980208

In recent years, depth estimation has witnessed significant advancements because of the development of deep learning. It's important to note that depth estimation tasks focus solely on predicting the depth of each pixel in an image and do not include object detection or object recognition. Depth estimation is the use of pixel transformations in the image to obtain distance information from each point in the scene to the camera to generate a depth map. Object detection is the process of classifying and localizing an image, given a picture, so as to identify the objects in the picture and determine their location. To overcome this limitation and integrate object detection into the depth estimation process, this paper proposes a novel self-supervised monocular depth estimation algorithm that leverages an attention mechanism. By combining object detection and depth estimation, a real-time multi-task model is designed to enable simultaneous detection and depth estimation of objects. The framework comprises four essential components: an object detection sub-network, a depth estimation sub-network, a lateral sharing unit, and an attention loss. These components work collaboratively to enhance distance estimation accuracy for objects and improve the object detection performance. Throughout experiments, it is evident that the proposed approach can effectively estimate distances to objects and enhances the accuracy of object detection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Inverse design and accurate optimization of layered structured seeding mechanism for sugarcane planters

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International Journal of Agricultural and Biological Engineering 2023年第6期16卷 107-115页

作者： Jiaodi Liu Qingli Chen Hongzhen Xu Yong Hua Xiaoman Wu Key Laboratory of Advanced Manufacturing and Automation Technology(Guilin University of Technology) Education Department of Guangxi Zhuang Autonomous RegionGuilin 541006GuangxiChina College of Mechanical and Control Engineering Guilin University of TechnologyGuilin 541004GuangxiChina

Existing sugarcane planters are difficult to have ideal seeding trajectory and motion attitude at the same time, and the speed is difficult to meet the requirements at the critical stage, resulting in poor stability, which ultimately makes it impossible to ensure that the sugarcane seeding is carried out in accordance with the agronomic requirements to ensure that the cane buds are oriented toward the wall of the seeding trench. Aiming at the second-order non-circular planetary gear system pendulum seeding mechanism of the planter, the paper innovatively adopts the combination of inverse design and multi-objective layered accurate optimization to solve the problems of attitude, speed and trajectory that do not meet the requirements of fixed-attitude seeding that still exists in the process of sugarcane seeding. The second-order non-circular planetary gear system is simplified into a three-rod two-degree-of-freedom mechanism, and the radius of the pitch curve of each non-circular gear is solved inversely by actively preplanning the static trajectory of the cane seed motion and analyzing the law of motion of the rod assembly. Determining the range of cane seed attitude angles in different motion phases as the first layer optimization objective, and fine-tuning the position of static trajectory key type value points to achieve the first layer optimization. Based on the non-circular gear pitch curve obtained from optimization, the interpolation points are marked on each non-circular gear pitch curve of the second-order non-circular planetary gear system, and based on the parameter optimization method of human-computer interaction, the radius values corresponding to the interpolation points of the non-circular gear pitch curve are fine-tuned to optimize the pitch curves, so as to satisfy the speed requirements of the cane species in each stage, and at the same time to make the convexity of non-circular gears in line with the principle of gear mesh, so as to complete the

关键词： sugarcane planting seeding mechanism inverse design layered structure optimization

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Interaction Quantification of MTDC Systems Connected with Weak AC Grids

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2088-2099页

作者： Wanning Zheng Jiabing Hu Li Chai Bing Liu Zixia Sang Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and TechnologyWuhan 430081China State Key Laboratory of Advanced Electromagnetic Engineering and Technology and School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China State Grid Hubei Economic Research Institute Wuhan 430077China

The small-signal stability of multi-terminal high voltage direct current(HVDC)systems has become one of the vital issues in modern power *** among voltage source converters(VSCs)have a significant impact on the stability of the *** paper proposes an interaction quantification method based on the self-/en-stabilizing coefficients of the general N-terminal HVDC system with a weak AC network ***,we derive the explicit formulae of self-/en-stabilizing coefficients for any N-terminal HVDC system,which can quantify the interactions through different paths *** relation between the self-/en-stabilizing coefficients and the poles of the system can be used to evaluate the impact of the interactions on the system stability ***,we employ the obtained formulae to analyze the parameter sensitivity and explain how a parameter affects the stability of the system through different paths of ***,extensive examples are given to demonstrate the effectiveness of the proposed method.

关键词： Interactions multi-terminal HVDC self-/enstabilizing coefficients small-signal stability

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