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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是241-250 订阅

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Establishment and Analysis of Passive Localization Model for a Class of Unmanned Aerial Vehicle systems 13

Establishment and Analysis of Passive Localization Model for...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Yu, Liwen Yang, Qianru Li, Yuqian Zhang, Ruikun School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

In order to avoid the interference of external magnetic field, UAV swarms should keep electromagnetic silence while flying, and using pure azimuth passive localization to adjust the position of UAVs is an effective method. the pure azimuth passive localization problem for UAVs includes two scenarios: known and unknown positions and quantities of UAVs emitting signals. For the case that the number and position of the three UAVs transmitting signals are known, a localization model based on the sine-cosine theorem, iteration and other methods for passively receiving signal UAVs is established for adjusting the position of the UAVs in order to realize the effective adjustment of the position of the UAVs. For the case that the number and quantity of some UAVs transmitting signals are unknown, this paper proposes an effective localization model of UAVs with a slight deviation of a certain position based on the polar coordinate localization method and the characteristics of the hidden circle model. Meanwhile, the relevant properties of the sine-cosine function are utilized to determine the uniqueness of the solution. Finally, the accuracy of the model is verified by numerical simulation. the results show that the theoretical credibility of the method proposed in this paper is strong and provides a more reliable theoretical support for the research in the field of UAV technology. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Cooperative control of Virtual Coupled High-Speed Trains with the Consideration of Ride Comfort: A Distributed Tube-Based Model Predictive control Approach 13

Cooperative Control of Virtual Coupled High-Speed Trains wit...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhu, Lei Zhang, Xijuan Huang, Deqing Xuefang, L. School of Electrical Engineering Southwest Jiaotong University Chengdu610031 China Co. Ltd Chengdu610092 China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (纸本)9798350361674

In this work, a distributed tube-based model predictive control (TMPC) method is proposed to address the cooperative control of virtual coupled high-speed trains (HSTs), which ensures the ride comfort of passengers. Firstly, a nonlinear MPC scheme is developed based on the train nominal dynamics to address the cooperative operation among multiple HSTs with different initial states. Especially, the constraints related to the passenger comfort, including the acceleration and the acceleration change rate, are transformed into the input and the input increment constraints of the MPC respectively, which ensures the ride comfort of the HSTs. Subsequently, the recursive feasibility and stability of the MPC are proved theoretically. Secondly, an auxiliary feedback controller is devised based on the actual train dynamics of the train to deal with the external disturbances while improving the operational stability. the auxiliary feedback controller, comprising a discrete-time sliding mode control (DSMC) and a discrete-time disturbance observer (DTDO), utilizes the optimal state sequences generated by the nonlinear MPC as a reference for the feedback control. Additionally, the stability of the auxiliary feedback controller is strictly proved. Finally, the effectiveness of the proposed distributed TMPC is verified through simulations. © 2024 ieee.

关键词： Sliding mode control

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SG-DLCT: Saliency-Guided Dual-Level Collaborative Transformer for Image Captioning 13

SG-DLCT: Saliency-Guided Dual-Level Collaborative Transforme...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhu, Xinchao Deng, Jingzheng Zhang, Kanjian Zhang, Jinxia Southeast University Shenzhen Research Institute Shenzhen518057 China School of Automation Southeast University The Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350361674

Improving the generation of feature representations for images has been a central focus in the field of image captioning. However, existing approaches, whether based on grid features or region features, only consider the features within individual regions without capturing the interactions between objects. In this paper, we propose a novel Saliency-Guided Dual-Level Collaborative Transformer (SG-DLCT), which leverages saliency information to guide the network in capturing the importance of different objects and their interrelationships. Concretely, we utilize a pre-trained RGBD salient object ranking model to obtain saliency information. Building upon this, we propose the Saliency Feature Enhanced Module (SFEM), which optimizes region features using saliency values as weights, thereby guiding the model to focus on image regions that are more meaningful for generating image captions. Additionally, we propose the Saliency-Guided Cross Attention (SGCA), which leverages saliency information to model the interaction between region features and grid features, allowing the final model's features to benefit from the advantages of both types of features. Experimental results validate the significance of saliency information in image captioning tasks and demonstrate the effectiveness of our proposed method. © 2024 ieee.

关键词： Image Captioning Salient Object Ranking Transformer

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Vertical control Methods of Maglev Train with Elastic Structural Model and Guideway Disturbances 13

Vertical Control Methods of Maglev Train with Elastic Struct...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Li, Haitao Huang, Chao Qiao, Cuncun Chen, Sen Zhao, Zhiliang Dou, Xiaoliang Crrc Qingdao Sifang Co. Ltd. State Key Laboratory of High-speed Maglev Transportation Technology Qingdao266000 China School of Mathematics and Statistics Shaanxi Normal University Xi'an710119 China School of Electrical and Control Engineering North Central University Taiyuan030051 China

ISBN: (纸本)9798350361674

this paper investigates the vertical control problem of maglev train. Firstly, vertical model of maglev train is constructed, including car body, levitation bogie, suspension electromagnet and guideway. the presented vertical model considers the elastic connections between components and the external disturbances affecting at the guideway. To achieve the vertical control objective, i.e., regulating the suspension gap, a linear active disturbance rejection control (ADRC) design is proposed. the proposed ADRC can deal with the guideway disturbances and the unknown elastic information. To apply ADRC technique to vertical control of maglev train, the procedures to determine the relative degree and the nominal input gain are quantitatively discussed. To illustrate the effectiveness of the proposed ADRC, the comparison with the control design in MBB train and PID control is given. © 2024 ieee.

关键词： Disturbance rejection

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Fault Diagnosis of Quadrotor UAV Propeller with Local-Global Attention-Based Graph Neural Networks 13

Fault Diagnosis of Quadrotor UAV Propeller with Local-Global...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Zhiwei Liang, Shaojun Sun, Ye Yang, Yi Wang, Yanwei Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Ordnance Sergeant Army Engineering University Wuhan430075 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan430073 China

ISBN: (纸本)9798350361674

With the wide application of Quadrotor Unmanned Aerial Vehicles (QUAV) in military and civilian fields, its flight reliability problem has become a hot research topic. the propellers, which are the important components relating to the performance of QUAV, play a central role in safe flight. Traditional data-driven fault diagnosis methods of QUAV are based on Euclidean distance, which cannot effectively capture the interrelationship of multiple variables. In this paper, we treat the fault diagnosis of QUAV propeller as a node classification problem in non-Euclidean infinite dimensional space by introducing the method of Local-Global Attention-based Graph Neural Network (LGA-GNN). First, the graph topology is constructed using the KNN method based on the real flight log of the QUAV. then, the local and global attention strategies are integrated to form a graph-based collaborative feature extraction model. Finally, a unified end-to-end fault diagnosis framework tailored for QUAV is proposed. In the experiment section, real flight data of QUAV with different propeller fault conditions are collected, and the effectiveness and superiority of the proposed method are verified by comparing with other methods based on real data. © 2024 ieee.

关键词： Graph neural networks

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Local Directional Centrality Clustering Based on K-nearest Neighbor Outlier Detection and Shared Neighborhood Strategy 13

Local Directional Centrality Clustering Based on K-nearest N...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Qing Feng, Zihang Yan, Liping Xia, Yuanqing School of Automation Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China

ISBN: (纸本)9798350361674

Clustering, as an important prerequisite method in data analysis, can uncover potential information in the data and then proceed to the next step of data analysis and processing. the recently proposed boundary-seeking Clustering algorithm using the local Direction Centrality (CDC) is a very effective method for clustering data with heterogeneous density and weak connectivity. However, it still has some shortcomings. On the one hand, the reachable distance is not enough to comprehensively distinguish weak connection situations, which can easily lead to cross cluster connection errors. On the other hand, K-nearest neighbor search is prone to cross cluster search, leading to misjudgment of boundary points and resulting in connection errors. this paper proposes a local direction centrality clustering algorithm based on K-nearest neighbor outlier detection and shared neighborhood strategy (SODCDC) for sparse and weakly connected data. this algorithm uses a K-nearest neighbor outlier detection strategy to relieve K-nearest neighbor cross cluster search and reduces the probability of boundary point misjudgment. At the same time, it uses a shared neighborhood strategy to further prevent cross cluster connections of weakly connected data. Experiments on some datasets have shown that compared to the original algorithm, the proposed algorithm performs better under the commonly used evaluation metrics. © 2024 ieee.

关键词： Nearest neighbor search

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Identification Modelling of the Hammerstein Nonlinear systems Utilizing Adaptive Neural Fuzzy Networks and State Space Model 13

Identification Modelling of the Hammerstein Nonlinear System...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhu, YanZong Yang, Yuesong Li, Feng Zhou, Zhou School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China

ISBN: (纸本)9798350361674

In this article, an identification modelling method for the Hammerstein nonlinear systems utilizing adaptive neural fuzzy networks and state space model is proposed. the proposed Hammerstein system consists of a five-layer adaptive neural fuzzy network approximating the static nonlinear block and a state space model representing the linear dynamical block with output noises, and identification of the adaptive neural fuzzy networks and the state space model are implemented independently by using designed combined signals, which include binary and random signals. Due to binary signals do not excite nonlinear systems, the recursive extended least squares algorithm is employed to identify the parameters of the state-space model and the colored noises model, which improves identification accuracy. Secondly, based on random signals input-output measurements, the cluster method and the recursive extended least squares algorithm are used to identify the adaptive neural fuzzy networks. the simulation results demonstrate the effective identification of the Hammerstein nonlinear system using the proposed parameter identification technique and the adaptive neural fuzzy networks can model the nonlinearity of the Hammerstein system. © 2024 ieee.

关键词： State space methods

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A Smooth Trajectory Planning for High Tibial Osteotomy Surgery with Robot-assisted Based on NURBS and S-curve Method 13

A Smooth Trajectory Planning for High Tibial Osteotomy Surge...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Li, Yuan Gao, Guanbin Bao, Yi Chen, Xinkai Guo, Xinyang Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China The First People's Hospital of Yunnan Province Kunming650034 China

ISBN: (纸本)9798350361674

Compared to conventional freehand procedures, robot-assisted surgery is more efficient and minimally invasive. the planning procedures decide the quality of robot-assisted surgery. However, the traditional polynomial function method and spline method can lead the robot to oscillate when executing the trajectory for puncture or location. To generate a smooth and steady trajectory to improve the quality of robot-assisted surgery, this paper proposes a trajectory planning method for the planning of puncture points in High Tibial Osteotomy (HTO). First, the coordinates of puncture points are obtained by creating a computer-aided design (CAD) model of the leg bone with the osteotomy plate. then, the non-uniform rational B-spine (NURBS)method is applied to generate a smooth trajectory for puncture points. the S-curve method is used to plan the velocity and acceleration. Finally, simulations are conducted to verify the correctness and effect of the proposed method, which show that the proposed method can improve the trajectory of surgery in terms of smoothness and jerk reduction. © 2024 ieee.

关键词： Robotic surgery

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Deep Metric learning with Impulsive Neural Network 13

Deep Metric Learning with Impulsive Neural Network

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Hong-Zhou Hu, Bin Chen, Long Guan, Zhi-Hong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Future Technology South China University of Technology Guangzhou510641 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350361674

Neural networks with siamese and triplet structures can discriminate between samples by explicitly learning the distance measure. But the dominant metric learning model based on deep neural networks (DNN) has issues with low energy efficiency and slow on-chip operating speed. Motivated by this, we put forth novel impulsive neural networks (INN) based deep metric learning approachs. the loss function is proposed based on the Euclidean distance between the membrane potentials of the neurons in the output layer of the sub-networks composed by Leaky integrate-and-fire (LIF) neurons. Furthermore, the back-propagation through time (BPTT) technique can be effectively implemented and utilized for network optimization through the surrogate functions. Experiments on multiple datasets show that the proposed model can learn the difference between images with performance very close to DNN-based models. At the same time, we conducted an intuitive energy consumption analysis of our model. the results show that compared with DNN-based models, the proposed model has higher energy efficiency. Besides, it only needs an extremely short time step T=10 to complete inference process, which makes the low-latency on-chip operation possible. © 2024 ieee.

关键词： Energy Efficiency Image Processing Impulsive Neural Network Metric learning

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Path-Follower-2: A Narrow Environment Navigation System for Large Mobile Inspection Robot 13

Path-Follower-2: A Narrow Environment Navigation System for ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wei, Shuang Chen, Junqiang Zhang, Muhua Gan, Yun Chen, Hongrui Huang, Deqing School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350361674

In industrial settings, mobile inspection robots, often large and heavy, are specifically designed for operation in confined environments. However, their considerable size often limits their ability to perform autonomous global path planning and self-directed obstacle avoidance. Typically, these robots adhere to pre-defined routes, executing high-precision maneuvers, halting for obstacles, and stopping at designated points for data collection. Maneuvering safely and efficiently in these restricted spaces is vital for their detection tasks and largely depends on their strict adherence to the pre-set global path. this paper introduces Path-Follower-2, a narrow environment navigation system for such robots, overcoming the limitations of existing methods that focus mainly on single functionalities like obstacle parking and avoidance. It integrates a novel obstacle descriptors, a global path preprocessing method for early narrow space detection, and adaptive collision detection for precise trajectory tracking in tight spaces. Tested in the Gazebo simulation against static and dynamic obstacles, Path-Follower-2 exhibits exceptional accuracy and smoothness in path tracking within narrow spaces, significantly surpassing most existing solutions. © 2024 ieee.

关键词： Navigation systems

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