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Trajectory tracking of 4-DOF assembly robot based on quantification factor and proportionality factor self-tuning fuzzy PID control

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International Journal of Wireless and Mobile Computing 2020年第4期18卷 361-370页

作者： Li, Cuiqiao Sun, Ying Li, Gongfa Jiang, Du Zhao, Haoyi Jiang, Guozhang Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

When the manipulator performs the assembling task, the performance of the system is easily affected by external interference and parameter changes, resulting in slow tracking response speed and low tracking accuracy. In order to improve the stability and robustness of the controller, a self-tuning fuzzy PID controller with quantification factor and proportionality factor is designed based on the fuzzy PID control. The parameters of the PID controller are dynamically fine-tuned by the fuzzy controller 1, and the quantification factor and proportionality factor are adjusted online by the modifying fuzzy controller 2 to fine-tune the parameters of PID controller. The control effect and robustness of the improved fuzzy PID controller to the manipulator system are simulated and verified on the MATLAB/Simulink platform. The simulation results show that the proposed method significantly improves the transient response speed, tracking accuracy and follower characteristics of the system, and has good dynamic performance. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Manipulators

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Bimanual gesture recognition based on convolution neural network

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International Journal of Wireless and Mobile Computing 2020年第4期18卷 311-319页

作者： Wu, Hao Li, Gongfa Sun, Ying Jiang, Guozhang Jiang, Du Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Deep learning

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Knowledge-driven material design platform based on the whole-process simulation and modeling

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 1-15页

作者： Gongzhuang Peng Tie Li Xiang Zhai Wenzheng Liu Heming Zhang National Engineering Research Center for Advanced Rolling Technology University of Science and Technology Beijing Beijing 100083 P.R.China Shanghai Electro-Mechanical Engineering Institute Shanghai 201109 P.R.China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing 100854 P.R.China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing 100854P.R.China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854P.R.China National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China

In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this *** this platform,the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level,the thermo-mechanical coupling field level and the microstructure evolution *** design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model,including customers’requirement knowledge,material component knowledge,process design knowledge and quality inspection knowledge,and utilizes the case-based reasoning approach to reuse the *** data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties,material components,and process parameters from historical samples,and utilizes multi-objective optimiza-tion algorithms to find the optimal combination of process *** of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

关键词： Product development knowledge-based engineering multi-scale simulation performance prediction model

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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A Novel Construction Method for 1-Dof Multi-Loop Kirigami Mechanisms

SSRN

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SSRN 2024年

作者： Lv, Weilin Nie, Wansui Wei, Jun Zhang, Jianjun Meng, Chuizhou Liu, Teng Sui, Jin Guo, Shijie Engineering Research Center The Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei University of Technology Tianjin300401 China Hebei Provincial Key Laboratory of Robot Perception and Human-Machine Fusion Hebei University of Technology Tianjin300401 China School of Mechanical Engineering Hebei University of Technology Tianjin300401 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Kirigami has been utilized in designing deployable mechanisms for various engineering applications. This paper presents a novel construction method for one-degree-of-freedom (1-DOF) multi-loop kirigami mechanisms whose folding motions are kinematically equivalent to their original degree-4 rigid origami. Firstly, we introduce a family of single-loop kirigami (SLK) as the main loop of 1-DOF multi-loop kirigami. Secondly, the coupling-constraint properties of multi-DOF SLKs are clarified, and the structural characteristics of the 1-DOF multi-loop kirigami are defined, based on which, the construction method for the 1-DOF multi-loop kirigami is proposed. Thirdly, the representative models are selected and their kinematics are presented to demonstrate their capability to achieve folding motion equivalent to their origami prototypes, thus verifying the validity of our proposed methodology. Fourthly, the non-constraint condition of the 1-DOF multi-loop kirigami is presented. Finally, we propose extended configurations of 1-DOF multi-loop kirigami and provide the forward and inverse design methods for constructing kirigami meshes. The proposed methodology facilitates the design of large-scale deployable structures. © 2024, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Sliding mode controller by using adaptive exponential reaching law based on nonlinear disturbance observer

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ISA Transactions 2025年

作者： Liu, Ying Li, Gongfa Du Jiang Yun, Juntong Tao, Bo Jiang, Guozhang Kong, Jianyi Chen, Baojia Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan 430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of science and Technology Wuhan 430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of science and Technology Wuhan 430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan 430081 China Hubei Longzhong Laboratory Xiangyang 441000 China Hubei Key Laboratory of Hydroelectric Machinery Design& Maintenance China Three Gorges University Yichang 443002 China

Aiming at the position tracking control problem of flexible jointed manipulator in the uncertainties and external disturbances, this paper proposes an integrated nonlinear disturbance observer (NDOB) and a non-singular fast terminal sliding mode (NFTSM) control method based on adaptive exponential reaching law (AERL). The NDOB is designed to ensure that the time-varying disturbance is suppressed in finite time. The AERL is designed to improve the track tracking speed and reduce the chattering of the control input. Simulation results show that compared with the optimal ARL, the proposed method reduces the trajectory tracking error by 1.3 %, improves the trajectory tracking convergence speed. Experimental results show that the proposed method reduces the trajectory tracking error by 0.545 %. © 2025 ISA

关键词： AERL AERL-NFTSM Chattering Convergence accuracy Flexible jointed manipulator

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Pulse Current Enhanced Toughness Mechanism of Double-Layer Hot-Rolled Nickel-Steel Clad Plates

SSRN

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SSRN 2024年

作者： Zhao, Guanghui Zhang, Yu Tang, Qingyao Li, Juan Ma, Lifeng Ma, Lianwei Taiyuan University of Science and Technology Taiyuan030024 China Shanxi Provincial Key Laboratory of Metallurgical Device Design Theory and Technology Taiyuan030024 China School of Intelligent Manufacturing Industry Shanxi University of Electronic Science and Technology Linfen041000 China CCTEG Taiyuan Research Institute SHANXI Taiyuan030006 China

To improve the comprehensive performance of Inconel825/X80 clad plates, pulse current electroplastic experiments were carried out on Inconel825/X80 hot-rolled clad plates at different current densities (22A/mm2, 26A/mm2, 30A/mm2) and various times (2min, 5min, 10min, 15min). The clad plate cross-section morphology, microstructure, and mechanical properties under different conditions were analyzed using a scanning electron microscope (SEM), electron backscatter diffraction (EBSD), transmission electron microscope (TEM), and tensile testing machine. The results showed that with the increase of current density and processing time, the tensile strength of the clad plate first increased and then decreased. The recovery and recrystallization degree of the matrix and the thickness of the interface reaction layer increased accordingly. Eventually, after pulse current treatment at 26A/mm2 and 10min, the dislocation density of the matrix decreased, and intermetallic compounds (IMCs) were formed. It led the Inconel825/X80 pipeline steel-clad plates to have excellent comprehensive performance, with an elongation of 43% and a tensile strength of 733 MPa. This finding has the potential to significantly enhance the effectiveness of post-rolling procedures applied to clad plates, leading to reduced manufacturing expenses. © 2024, The Authors. All rights reserved.

关键词： Tensile strength

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Transformer-based 3D range-gated imaging method with multiple depth priors

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Optics and Laser technology 2025年 190卷

作者： Liu, Xiaoquan Zhang, Di Gao, Jinming Wang, Xinwei Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Optoelectronics System Laboratory the Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronics Technology University of Chinese Academy of Sciences Beijing100049 China School of Electronic Electrical Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

3D Range-gated Imaging (3DRGI) has great potential for long-range detection in adverse weather and nighttime low-light conditions. However, the performance of traditional optical-based 3DRGI methods is highly dependent on hardware characteristics. Although existing vision-learning-based methods have mitigated these hardware limitations, their accuracy remains constrained. Our recent work has demonstrated that integrating optical depth-prior derived from range-gated imaging into vision-learning-based 3DRGI models can combine the advantages of optical-based and vision-learning-based methods and enhance the accuracy of depth maps. In this paper, we propose three architectural and prior innovations that collectively lead to significant improvements in 3DRGI methods. First, we refine the optical depth-prior to eliminate significant noise caused by multipath effects. Second, we introduce a transformer-based architecture that learns adaptive depth intervals under the supervision of the optical depth-prior to further design an optical-prior guided loss function. Finally, we introduce a depth perception prior and incorporate it into the vision learning model, further enhancing the overall depth estimation accuracy. The proposed method effectively integrates depth cues from multiple depth priors into a transformer-based architecture and significantly enhances the overall accuracy. Comparison experiments and ablation studies were conducted to validate the feasibility of the proposed method, and the results demonstrate that the proposed architecture achieves state-of-the-art performance. The results show that root mean square error achieved 45% to 60% improvement compared with Gated2Depth under harsh weather scenarios. © 2025

关键词： Mean square error

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An Underwater Acoustic Signal Denoising Method for Broadband Interference from Underwater Platforms Based on the Deep Neural Network Model

An Underwater Acoustic Signal Denoising Method for Broadband...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Linxuan Cao Zhixiong Lei Zihao Zhou Jie Sun Yang Shi Shaanxi Key Laboratory of Underwater Information Technology School of Marine science and technology Northwestern Polytechnical University Xi'an China Southwest China Institute of Electronic Technology Chengdu China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

As underwater vehicles widely used in ocean observation, the self-noise generated from the underwater platforms will affect the underwater acoustic signal observation, impeding signal detection. In this paper, we proposed an ideal binary mask (IBM) based Deep Neural Network (DNN) denoising method to reduce the broadband transient interference that underwater platforms caused. First, we analysis the interference produced by the underwater glider, illustrating its hazard by a common noisy situation. Then the IBM based DNN model was train to reduce this interference. Compared with spectral subtraction and Wiener filtering method, the method we proposed has short processing time of 0.15 $s$ in average, 7 dB higher signal-to-interference ratio (SIR) gain, and more complete retention of the original signal components in low SIR situations.

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