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共 716 条记录，以下是481-490 订阅

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

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

引用

data driven control and learning systems (ddcls)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Robust control learning systems Damping Magnetic levitation vehicles PI control control design Levitation

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A Novel YOLOv8-Pose-Based Algorithm for Abnormal Behavior Analysis of Quadruped Robots

A Novel YOLOv8-Pose-Based Algorithm for Abnormal Behavior An...

引用

data driven control and learning systems (ddcls)

作者： Shida Liu Tianzhen Zhang Honghai Ji Li Wang School of Electrical and Control Engineering North China University of Technology Beijing People's Republic of China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a novel abnormal behavior analysis algorithm based on YOLOv8 is proposed for the target recognition problem of guide robot dog. the algorithm is based on the human posture estimation model of YOLOv8 for abnormal behavior analysis, and by extracting the coordinate information of the key points of the human body, it can make conditional decisions on abnormal behaviors such as falling forward, backward and sideways, so as to realize the real-time monitoring of the blind people and send out alarms in time. the proposed algorithm ensures the accuracy and speed of the recognition while not taking up a lot of computational resources of the computer. the proposed method is applied to a real quadruped robot platform, and the effectiveness and applicability of the proposed algorithm is verified through a large number of experiments, which in this paper are based on the actual data collected from different human posture pictures.

关键词： Target recognition Robot kinematics Robot vision systems Blindness Real-time systems Skeleton Behavioral sciences

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data-driven Adaptive Predictive Cooperative control for Multi-Robot Aircraft Skin Measurement

Data-Driven Adaptive Predictive Cooperative Control for Mult...

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data driven control and learning systems (ddcls)

作者： Xueming Zhang Haoran Tan Xing Jiang Yaonan Wang Gang Wang College of Electrical and Information Engineering Hunan University Yuelu District Changsha Hunan P. R. China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha P. R. China School of Automation Beijing Institute of Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a novel approach to addressing the relative position problem in aircraft skin measurement based on data-driven predictive cooperative control (DDPCC) for multi-robot systems. Specifically, the study focuses on the interaction between two compound mobile robots, namely the tracker and scanner, and transforms the relative position problem into a formation control problem involving two robot chassis and a virtual leader. Leveraging the principles of predictive control, we introduce a control input criterion function, by incorporating the cross-tracking error between each robot and its neighbors, as well as its anticipated control input increment in the future. It turns out that the controller design and stability analysis of this DDPCC approach do not require explicit mathematical models. through extensive simulations, we demonstrate the effectiveness and practicality of the proposed strategy.

关键词： Transforms Position measurement control systems Skin Mathematical models Stability analysis Multi-robot systems

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ESO-Based data driven Set-Point learning control for Nonlinear batch Processes with PD-Type Feedback control Structure Subject to Nonrepetitive Uncertainties*

ESO-Based Data Driven Set-Point Learning Control for Nonline...

引用

data driven control and learning systems (ddcls)

作者： Naseem Ahmad Shoulin Hao Tao Liu Yihui Gong Jiyan Zhang Haixia Wang Yong Zhu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Institute of Advanced Control Technology Dalian University of Technology Dalian P. R. China School of Biomedical Engineering Dalian University of Technology Dalian P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. the controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. the effectiveness and superiority of the proposed scheme are verified by an illustrative example.

关键词： learning systems Uncertainty Heuristic algorithms Batch production systems Process control Observers Iterative algorithms

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Lane Keeping Method based on MIMO Model-Free Adaptive control

Lane Keeping Method based on MIMO Model-Free Adaptive Contro...

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data driven control and learning systems (ddcls)

作者： Weifeng Zhai Zichao Zhang Dehui Sun Academy of Electrical and Control Engineering North China University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to prevent drivers from lane deviation due to distraction or incorrect operation and reduce the probability of traffic accidents, so the research on lane keeping is of great significance. In this paper, a lane keeping method based on MIMO model-free adaptive control is proposed. Traditional lane keeping control methods all need to establish the mathematical model of the controlled vehicle, however, the establishment of the mathematical model of the controlled vehicle needs to take into account a number of factors and physical principles, such as vehicle dynamics, environmental factors, etc., which ultimately results in the lack of precision in the establishment of the model. the lane keeping control method proposed in this paper has the following characteristics: the design of the controller does not need the mechanistic model of the controlled vehicle, and only the input and output data generated by the controlled vehicle is needed to design the controller, which can realize the control of the controlled vehicle, and the control system is easy to design, with a small amount of computation, and easy to be realized in the field. Finally, it is concluded through experiments that the proposed method has better lane keeping control effect compared with PID control algorithm.

关键词： Jacobian matrices Adaptation models PI control Computational modeling control systems Mathematical models data models

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Development of Neighborhood Double Preserving Autoencoder Models for Soft Sensors in Industrial Processes

Development of Neighborhood Double Preserving Autoencoder Mo...

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data driven control and learning systems (ddcls)

作者： Tianyi Zhou Xiaolong Ye Xi Hong Chihang Wei School of Information Science and Technology Hangzhou Normal University Hangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, soft sensors have been widely applied in the process industry. Autoencoder (AE) models possess the capability to extract deep features, which would be important for constructing regression models for predicting quality variables. It is reported that preserving the neighborhood structure of the original data is also important. However, existing AE models rarely consider this issue, and we would like to make the models more capable of neighborhood structure preservation based on manifold learning concepts. To solve this problem, a novel neighborhood double preserving autoencoder (NDPAE) is proposed in this paper. Different from the traditional AE, a novel loss function is established by introducing a double constraint term. this model can extract the neighborhood structure of data while maximizing the retention of its information, and more accurately predict the quality variables. the experimental results on the industrial sulfur recovery unit (SRU) process demonstrate that the proposed NDPAE model is more effective compared with other common models.

关键词： learning systems Industries Soft sensors Process control Predictive models Feature extraction data models

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FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models

FedAWS: A Federated Tuning Approach with Adaptive Weight Shr...

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data driven control and learning systems (ddcls)

作者： Yubin Cheng Zhihuan Song Xiaoyu Jiang Le Yao State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Mathematics Hangzhou Normal University Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, pre-trained foundation models have developed rapidly and have been applied to various downstream tasks through fine-tuning methods. However, due to the existence of data silos, the lack of data for fine-tuning has become a vital issue preventing the application of the foundation model to industry. In addition, the high complexity of the network makes the foundation model prone to overfitting when used for data-constrained industrial tasks. therefore, a federated tuning approach with adaptive weight shrinking, named FedAWS, is proposed for collaborative fine-tuning between factories. FedAWS adaptively selects the weight-shrinking factor to reduce overfitting and improve the generalization of the global model without relying on additional data sets or human experience. the case study on an industrial dataset demonstrates the effectiveness of FedAWS. this successful validation provides a new way for inter-factory collaboration to apply the foundation model to industry, thus facilitating the efficient utilization of industrial data.

关键词： Industries learning systems Adaptation models Collaboration control systems data models Production facilities

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the Forecast of Health Human Resources During the Period of “Healthy China 2030” in Shandong Province

The Forecast of Health Human Resources During the Period of ...

引用

data driven control and learning systems (ddcls)

作者： Luyu Zang Hongyan Li Peng Gao School of Management Shanghai University of Engineering Science Shanghai

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Objective: To analyze the development status of health human resources in Shandong Province from 2009 to 2021, and predict the development trend of health human resources in Shandong Province during the “Healthy China 2030” period. Method: Based on the relevant data on health human resources from 2009 to 2021, a grey GM (1,1) prediction model was constructed to predict and analyze the allocation of health human resources in Shandong Province from 2022 to 2030. Result: by 2030, the number of health personnel, health technicians, licensed (assistant) physicians, and registered nurses reached 1.56 million, 1.41 million, 585000, and 751000, respectively; the number of licensed (assistant) physicians and registered nurses per thousand population will reach 5.44 and 6.98 respectively; the healthcare ratio will reach 1:1.28. Conclusion: By 2030, the number of health human resources in Shandong Province will increase, but the number of nursing staff still cannot alleviate the shortage of health human resources and the unreasonable proportion of medical staff in Shandong Province.

关键词： learning systems Sociology Medical services Predictive models Market research control systems data models

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An Online Evaluation Based Extended-State Kalman Filter Approach to Navigation Via Signal of Opportunity

An Online Evaluation Based Extended-State Kalman Filter Appr...

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data driven control and learning systems (ddcls)

作者： Junyuan Liu Xuanwen Chen Sen Chen Wenyan Bai Yinghui Wang Zhiliang Zhao Airborne Avionics Flight Test Institute Chinese Flight Test Establishment Xi'an China School of Mathematics and Statistics Shaanxi Normal University Xi'an China Beijing Aerospace Automatic Control Institute Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing China School of Electrical and Control Engineering North Central University Taiyuan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper studies the problem of multi-source opportunistic signal navigation of aircraft. Firstly, the paper provides the mathematical models for five types of opportunistic signals. Furthermore, the online evaluation algorithm based on T-test is proposed for seeking the biased opportunistic signals. By disposing of the opportunistic signals causing intolerant location error, an estimated location position is obtained by trusty opportunistic signals. Finally, to handle the unknown part of flight acceleration caused by external disturbances, the extended-state Kalman filter is constructed to promote the locating accuracy. the simulation results show the effectiveness of the proposed method.

关键词： learning systems Filtering Heuristic algorithms Simulation Filtering algorithms control systems Mathematical models

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Efficient Fusion and Task Guided Embedding for End-to-End Autonomous Driving

Efficient Fusion and Task Guided Embedding for End-to-End Au...

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data driven control and learning systems (ddcls)

作者： Yipin Guo Yilin Lang Qinyuan Ren College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To address the challenges of sensor fusion and safety risk prediction, contemporary closed-loop autonomous driving neural networks leveraging imitation learning typically require a substantial volume of parameters and computational resources to run neural networks. Given the constrained computational capacities of onboard vehicular computers, we introduce a compact yet potent solution named EfficientFuser. this approach employs EfficientViT for visual information extraction and integrates feature maps via cross attention. Subsequently, it utilizes a decoder-only transformer for the amalgamation of multiple features. For prediction purposes, learnable vectors are embedded as tokens to probe the association between the task and sensor features through attention. Evaluated on the CARLA simulation platform, EfficientFuser demonstrates remarkable efficiency, utilizing merely 37.6% of the parameters and 8.7% of the computations compared to the state-of-the-art lightweight method with only 0.4% lower driving score, and the safety score neared that of the leading safety-enhanced method, showcasing its efficacy and potential for practical deployment in autonomous driving systems.

关键词： learning systems Visualization Sensor fusion Transformers Vectors Safety Computational efficiency

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