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2024 China automation Congress, CAC 2024

作者： Li, Wen Zhang, Xinhao He, Weitian Zou, Chaobin Yang, Qinchen Zhang, Fukai Cheng, Hong Wang, Cong School of Control Science and Engineering Shandong University Jinan China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368604

With the intensification of population aging and the increasing number of patients with lower limb dysfunction, the research and precise control of lower limb rehabilitation robots have become increasingly important. In this study, a sitting and lying lower limb exoskeleton robot model is developed based on the CoppeliaSim platform. For the nonlinear system of a two-degree-of-freedom single-leg robot with uncertainty, an improved PID control method based on deterministic learning is proposed. Under the condition of persistent excitation, this control method accurately identifies the unknown dynamic characteristics of the system by introducing a neural network, ensuring precise control of lower limb movements. Learning-based PID controllers are constructed using the gained knowledge to achieve fast, smooth tracking and greatly improve the safety of rehabilitation control. The results of simulation experiments show that the proposed control strategy in this paper is effective. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Comparative Analysis of Vibration and Noise of Axial Flux Motor with Different Pole and Slot Combinations

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Chinese Journal of Electrical engineering 2023年第4期9卷 144-156页

作者： Qixu Chen Zhizhong Chen Guoli Li Dechen Wu Peng Li School of Electrical Engineering and Automation Anhui UniversityHefei 230601China National Engineering Laboratory of Energy-Saving Motor&Control Technology Anhui UniversityHefei 230601China

The electromagnetic vibration noise in axial flux motors was meticulously *** this study,24-slot/10-pole and 12-slot/10-pole axial flux motors were chosen as the subjects of *** spatial characteristics of the axial electromagnetic force were derived analytically and confirmed via two-dimensional Fourier *** finite-element method was used to simulate the low-order axial modes of both ***,a modal experiment on the stator of a 24-slot/10-pole axial flux motor was conducted to validate the simulation’s *** integrating the electromagnetic and structural models,a comprehensive multi-physical field model was developed to calculate the vibration noise of the axial flux *** precision of this model was subsequently corroborated with noise *** findings from this study aim to offer insights into identifying the sources of vibration noise in axial flux motors.

关键词： Axial electromagnetic force vibration noise axial flux motors modal analysis

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2D Human Pose Estimation with Residual Upsampling and Spatial Feature Enhancement 43

2D Human Pose Estimation with Residual Upsampling and Spatia...

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43rd Chinese control Conference, CCC 2024

作者： Li, Shanbin Zheng, Xishan South China University of Technology School of Automation Science and Engineering Guangzhou China Changji University School of Energy and Control Engineering Xinjiang China

ISBN: (纸本)9789887581581

Computer vision, which has been extensively used in intelligent monitoring, self-driving, medical assistance, sports analysis, and other fields, includes human pose estimation as a key component. Human pose estimation algorithms have been appearing nonstop in recent years, and as they get more complex and structured, it becomes harder to analyze and actually implement the algorithms. SBL network is surprisingly efficient for such a simple design. This study makes several modifications based on SBL to further enhance its performance. In order to fuse rich features from the feature extraction stage to the upsampling phase, we first introduce the residual upsampling module (RUSM). This guarantees that more detailed, high-resolution information can be utilized to upsampling features, significantly increasing the accuracy of joint recognition in the human body. Additionally, we present the spatial feature enhancement module (SFEM), which utilizes the spatial attention mechanism with the residual structure. It is employed to boost spatial features of the upsampling phase and further increase the detection precision of the network. Lastly, we conduct numerous experiments on the LSP dataset, which amply illustrates the enhanced functionality of the extra modules and the better performance of our suggested network. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Image enhancement

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A REVIEW OF PAVEMENT DEFECT DETECTION BASED ON VISUAL PERCEPTION

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and Applied Mechanics 2024年第17期2024卷 131-146页

作者： Yang, Chenchen Yang, Li Duan, Hailong Deng, Jingwei School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin300222 China

The inspection of roads for defects, as basic transportation infrastructure, is critical to maintain the efficient and safe operation of the transportation system. Visual perception-based pavement defect detection methods can efficiently and accurately identify pavement issues and provide data support for intelligent preventive maintenance. This paper summarizes the development of pavement defect detection technology, from feature extraction to automatic feature extraction. Deep learning-based pavement defect detection methods are analyzed and compared in three aspects: classification, object detection, and segmentation. By integrating recent research methods, the paper further reviews innovative approaches to pavement defect detection modeling in four areas: information flow models, Transformer models, attention mechanisms, and the application of 3D technology. Additionally, various defect types, pavement defect datasets, and defect detection systems are introduced. Finally, the paper provides an outlook on future trends and summarizes the key findings. © 2024, Cefin Publishing House. All rights reserved.

关键词： Preventive maintenance

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Integrated Multimodal Fault Diagnosis of Industrial Gearboxes Using Vibration Signals and Infrared Thermal Imaging 15

Integrated Multimodal Fault Diagnosis of Industrial Gearboxe...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Cao, Xunqi Li, Yan Yu, Kun Zhang, Yongchao School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (纸本)9798350354010

With the increasing demand for precision in fault diagnosis of gearboxes in complex systems, diagnostic methods based solely on vibration signal characteristics have shown limitations. Therefore, multimodal information fusion technology has become a new trend in the field of gearbox fault diagnosis. This article innovatively proposes a multimodal diagnostic method that combines vibration signals with thermal images, aiming to use thermal imaging technology to intuitively capture the thermal changes during gearbox operation and integrate vibration signals to comprehensively reflect fault characteristics. By introducing Convolutional Neural Networks (CNN) for feature depth extraction, this method effectively improves the parsing ability of fault information, thereby enhancing the accuracy and reliability of fault diagnosis. Experimental data confirms that compared to diagnostic methods using a single signal source, the method proposed in this paper performs well in fault detection of helical gearboxes, significantly improving diagnostic accuracy and precision. © 2024 IEEE.

关键词： Convolutional neural networks

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Distributed Adaptive Inverse Differential Game Approach to Leader's Behavior Learning for Multiple Autonomous Followers

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1666-1678页

作者： Wu, Huai-Ning Wang, Mi Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Peng Cheng Laboratory Shenzhen518000 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

In this article, the leader's behavior learning problem is investigated for a class of leader-follower systems, where the leader's behavior is assumed not a priori known to all followers that are autonomous. A multiplayer nonzero-sum differential game is introduced to indicate the control interaction between agents, where the leader's behavior is modeled by an unknown cost function. The autonomous followers aim to retrieve the weighting matrix in the leader's cost function collaboratively. A distributed online adaptive inverse differential game (IDG) approach to the leader's behavior learning is proposed for the autonomous followers. Specifically, a concurrent learning based adaptive law and an interactive game controller are first developed for each autonomous follower to learn the leader's feedback gain matrix online, while the feedback Nash equilibrium of the game can be achieved. Then, a linear matrix inequality optimization problem is formulated to determine the weighting matrix of the leader's cost function for each autonomous follower. The proposed method simply requires that all followers share their interactive feedback gain matrices, not their private intents, and use only the system state data, not both the system state data and the leader's control input data. The main advantages of the proposed method are that it can be implemented online without requiring the persistent excitation condition and needs less computational power for all followers. Finally, numerical simulations are provided to demonstrate the effectiveness and feasibility of the developed method. © 2020 IEEE.

关键词： Cost functions

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Coronary Artery Segmentation in X-ray Angiography Based on Deep Learning Approach 43

Coronary Artery Segmentation in X-ray Angiography Based on D...

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43rd Chinese control Conference, CCC 2024

作者： Li, Shanbin Fan, Yiting South China University of Technology School of Automation Science and Engineering Guangzhou China Changji University School of Energy and Control Engineering Xinjiang China

ISBN: (纸本)9789887581581

Segmenting coronary arteries from X-ray coronary angiography (XCA) images allows observation of coronary artery morphology and stenosis, which is of great significance for computer-aided diagnosis and coronary artery disease treatment. However, XCA images have low contrast and irregular lighting due to the limits of existing imaging techniques, making vascular segmentation challenging. Segmentation of coronary arteries using conventional U-shaped segmentation networks is challenging due to the problem of major differences in vascular morphology, size, and overlap. This paper proposes a network based on the U-shape structure for multiscale context information fusion with lightweight GhostNetV2 as the backbone feature extraction network to improve the model's feature extraction capabilities. Then a multi-scale context fusion module (MCF) is proposed to effectively capture the contextual information of blood vessels. Finally, we propose a feature re-extraction module (FRM) to achieve effective fine feature re-extraction in complex backgrounds. Experimental results show that the model we propose achieves more accurate coronary artery segmentation in complex backgrounds with fewer parameters, improves the segmentation of fine vessels at the end of coronary arteries, and performs well compared with other artery segmentation models. The F1 score, Intersection over Union (IOU), accuracy (ACC), and Sensitivity (Sen) of the model can reach 82.23 %, 69.83 %, 98.79 %, 81.93 %, respectively. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Deep learning

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Learning-Observer-Based Adaptive Tracking control of Multiagent Systems Using Compensation Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 358-369页

作者： Li, Zhiqiang Liu, Yang Ma, Hui Li, Hongyi Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Mathematics and Statistics Guangzhou510006 China

This article focuses on the adaptive consensus tracking problem for nonlinear multiagent systems with unmeasurable state and input saturation. For unmeasurable state and nonlinearities, a learning-observer inspired by the idea of extended state observer and the learning ability of neural network are established to observe the reconstructed system. In view of input saturation, a nonlinear auxiliary system with the same order is constructed to obtain the compensation signals. Different from the previous auxiliary system, the proposed method simultaneously compensates the change of nonlinearity caused by input saturation. Subsequently, a consensus tracking error is designed to ensure that the follower tracks the neighbor compensated output. Moreover, an adaptive consensus tracking control method based on learning-observer and compensation mechanism is proposed under the backstepping framework. The stability analysis is given by virtue of the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through the simulation of unmanned air vehicles with one virtual leader and four followers. © 2020 IEEE.

关键词： Neural networks

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Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

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Frontiers of Mechanical engineering 2023年第3期18卷 123-143页

作者： Yiming YAN Shuting WANG Yuanlong XIE Hao WU Shiqi ZHENG Hu LI School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China

To achieve the collision-free trajectory tracking of the four-wheeled mobile robot(FMR),existing methods resolve the tracking control and obstacle avoidance *** the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is *** address this challenge,this paper proposes an obstacle-circumventing adaptive control(OCAC)***,a novel anti-disturbance terminal slide mode control with adaptive gains is formulated,incorporating specified control laws for different *** formulation guarantees rapid convergence and simultaneous chattering *** introducing sub-target points,a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one,thereby reducing the burden of local path searching while ensuring system stability during obstacle *** experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

关键词： four-wheeled mobile robot obstacle-circumventing adaptive control adaptive anti-disturbance terminal sliding mode control sub-target dynamic tracking regression obstacle avoidance

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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