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Well-Conditioned Identifiability Criteria with Observability Degree for Parameter Estimation of Permanent Magnet Synchronous Motors

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IEEE Transactions on Transportation Electrification 2024年第3期11卷 7195-7206页

作者： Wang, Xinghao Jing, Hao Chen, Zifeng Luo, Dehui Zhao, Hang Wang, Xueqing Xiao, Dianxun Robotics and Autonomous Systems Thrust Guangzhou511457 China Sichuan University College of Electrical Engineering Chengdu610065 China Sustainable Energy and Environment Thrust Guangzhou511457 China The Hong Kong University of Science and Technology Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen518048 China

The identifiability of parameters in permanent magnet synchronous motors (PMSMs) depends on the operating conditions. Due to disturbances and noise in actual systems, identifiability conditions that rely solely on traditional observability theorems for parameter estimation are ill-conditioned under certain operating environments, which cannot provide a definite reference for accurate outcomes. This paper introduces a novel theorem regarding the degree of observability to bridge the gap in traditional parameter observability analyses, and defines a quadratic function to create a unique information matrix based on the PMSM voltage model. As an example, a practical observability condition is derived for the inductances of the dq-axis of PMSMs by applying the proposed theory to the information matrix, offering guidance for selecting identifiable operating conditions in real-time systems. Moreover, comprehensive analyses are conducted to theoretically verify the correctness and stability of the proposed observability condition. Finally, experiments are performed to validate the feasibility of the proposed observability theorem for parameter estimation. © 2015 IEEE.

关键词： Permanent magnets

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet 42

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electronic nose

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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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Computer systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Kinematic Parameter Calibration for Parallel Robot Based on Robust Estimation and PCR Algorithm 7

Kinematic Parameter Calibration for Parallel Robot Based on ...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Wang, Jin Zhang, Renge Hu, Weijun Zhang, Haiyun Lu, Guodong Chen, Silu Luo, Jingbo School Of Mechanical Engineering Zhejiang University State Key Laboratory Of Fluid Power And Mechatronic Systems Hangzhou310027 China School Of Robotics Ningbo University Of Technology Ningbo315211 China Robotics Institute Of Zhejiang University Hangzhou310027 China Zhejiang Qiantang Robot And Intelligent Equipment Research Co. Ltd Hangzhou310018 China Ningbo Institute Of Materials Technology And Engineering Chinese Academy Of Sciences Ningbo315201 China

ISBN: (纸本)9798350361445

Due to the existence of redundant identification parameters and parallel branch chains, the nonlinear error model of parallel robot is likely to produce multicollinearity, which will lead to the undesirable ill-conditioned identification matrix. Though PCR method can modify the ill-conditioned identification matrix, it is still susceptible to the gross error of the original data. In terms of these problems, this paper proposes a parameter calibration algorithm for parallel robot based on robust estimation and principal component regression (RE-PCR). Firstly, the linear identification model is established based on the closed-loop vector chain perturbation theory to improve the identification accuracy. On this basis, for the configuration types with redundant identification parameters and parallel branch chains, PCR is used to modify the ill-conditioned matrix and a robust estimation (RE) algorithm is designed to improve the resistance capacities of PCR to gross error data points. Finally, through the identification experiment based on 4PPa-2PaR parallel robot, the proposed algorithm demonstrates higher efficiency and stronger robustness in comparison with traditional methods. © 2024 IEEE.

关键词： Parameter estimation

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Three-Dimensional Trajectory Planning for Quadrotor UAV Based on Simplified PSO Algorithm 3rd

Three-Dimensional Trajectory Planning for Quadrotor UAV Base...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Yin, Ruhui Jiang, Han Shu, Zilin Ren, Ziang Lin, Qinghao Huang, Zixin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9789819624676

Aiming to address the requirements of Quadrotor Unmanned Aerial Vehicle (QUAV) for conducting mission cruises in three-dimensional complex environments, this paper introduces an optimization algorithm for trajectory planning. By analyzing the motion of the QUAV, the dynamics model is established, along with the constraint model for its flight trajectory. Based on the basic Particle Swarm Optimization (PSO) algorithm, simplified Particle Swarm Optimization (sPSO) removes the particle velocity term from the evolution equation. This adjustment helps prevent slower convergence and lower accuracy in the later stages, which can occur due to the dispersion of particles caused by the particle velocity term. The trajectory is smoothed by using the Minimum Snap principle to ensure that it meets the flight constraints of the QUAV. The simulation results demonstrate that the sPSO algorithm is effective and feasible for application in QUAV three-dimensional trajectory planning. In comparison to the PSO algorithm, the trajectory is more optimal, and the convergence accuracy is higher. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection

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Journal of Rock Mechanics and Geotechnical engineering 2022年第4期14卷 1140-1152页

作者： Zhenhao Xu Wen Ma Peng Lin Yilei Hua Geotechnical and Structural Engineering Research Center Shandong UniversityJinan250061China School of Qilu Transportation Shandong UniversityJinan250061China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan430000China

An intelligent lithology identification method is proposed based on deep learning of the rock microscopic *** on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were *** shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be *** the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative *** expanded a self-built dataset by using transfer learning and data ***,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model *** results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing ***,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology *** proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.

关键词： Deep learning Rock microscopic images Automatic classification Lithology identification

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A review of structures,verification,and calibration technologies of space robotic systems for on-orbit servicing

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Science China(Technological Sciences) 2021年第3期64卷 462-480页

作者： DING XiLun WANG YeCong WANG YaoBing XU Kun Robotics Institute Beihang UniversityBeijing 100191China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and ApplicationsBeijing 100094China

Recently,with the rapid development of aerospace technology,an increasing number of spacecraft is being launched into ***,the demands for on-orbit servicing(OOS)missions are rapidly *** robotics is one of the most promising approaches for various OOS missions;thus,research on space robotics technologies for OOS has attracted increased attention from space agencies and universities *** this paper,we review the structures,ground verification,and onorbit kinematics calibration technologies of space robotic systems for ***,we systematically summarize the development of space robotic systems and OOS programs based on space ***,according to the structures and applications,these systems are divided into three categories:large space manipulators,humanoid space robots,and small space *** to the capture mechanisms adopted,the end-effectors are systematically ***,the ground verification facilities used to simulate a microgravity environment are summarized and ***,the on-orbit kinematics calibration technologies are discussed and analyzed compared with the kinematics calibration technologies of industrial manipulators with regard to four ***,the development trends of the structures,verification,and calibration technologies are discussed to extend this review work.

关键词： space robotic system on-orbit servicing large space manipulator humanoid space robot verification kinematics calibration

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods 15

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechtronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India University of Tunis El Manar le Belvédère Laboratory of Robotics Informatics and Complex Systems RISC Lab-LR16ES07 National Engineering School of Tunis Tunis1002 Tunisia

ISBN: (纸本)9798350370249

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ( 91%) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ( 77.7%). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion. © 2024 IEEE.

关键词： Adversarial machine learning

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