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AUTONOMOUS RACING BASED ON MPC WITH ADAPTIVE LEARING PID FEEDBACK 9

AUTONOMOUS RACING BASED ON MPC WITH ADAPTIVE LEARING PID FEE...

引用

9th International Symposium on Test Automation and Instrumentation, ISTAI 2022

作者： Ding, Bingchao Wang, Liyong Su, Qinghua Zhang, Zheng Xu, Xiaoyu Wang, Chao Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781839538391

Trajectory tracking accuracy is of great significance for the control and safety management of autonomous vehicles. The curve with large curvature is often the bottleneck of vehicle control and the obstacle of safe driving. Aims at the problem of partial visual field loss and low control accuracy of formula car in the process of driving on the curve with large curvature. This paper presents a trajectory planning and tracking framework, using the improved Delaunay triangle subdivision algorithm in cone bucket guide track path planning and real-time planning, combined with the neural network PID feedback the MPC control algorithm for global path tracking, experimental results show that the success rate of the improved path planning algorithm is increased by 55.8% on large curvature curves;The tracking control algorithm is superior to the traditional control algorithm in terms of tracking accuracy, which can reduce the steady-state error caused by the simplicity of the superior model and make the steady-state error close to zero faster. Compared with the traditional MPC, the overall average tracking accuracy is improved by 35.78% and the maximum lateral error is reduced by 52.99%. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Motion planning

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THE ADAPTIVE MPC controlLER OF AUTONOMOUS DRIVING FORMULA FOR TRAJECTORY TRACKING 10

THE ADAPTIVE MPC CONTROLLER OF AUTONOMOUS DRIVING FORMULA FO...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Chen, Bowen Su, Qinghua Song, Yue Yin, Zehao Wang, Liyong Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

Autonomous driving technology has gradually been applied in real life, and it has also been improved in the China Collegiate Electric Vehicle Racing. However, as one of the critical components of autonomous driving technology, trajectory tracking control still faces challenges. This paper designs a trajectory tracking controller by building a vehicle's three-degree-of-freedom dynamics model and adopting adaptive model predictive control. It adjusts the prediction horizon of the vehicle's trajectory tracking controller in real-time based on the curvature of the desired trajectory to improve the effect of trajectory tracking. Experimental results show that the tracking effect of the adaptive prediction time-domain trajectory tracking controller in the S-curve is improved by 4.97% and 11.36% compared to the fixed prediction time-domain trajectory tracking controller, respectively. For the formula track conditions, the tracking effect of the controller proposed in this paper is improved by 0.71% compared to the fixed prediction time-domain trajectory tracking controller, with an average tracking error of 5.65%. © The Institution of Engineering & technology 2024.

关键词： Adaptive control systems

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REVIEW OF RESEARCH ON OCCLUSION ISSUES IN RGB-D DYNAMIC SCENE 3D RECONSTRUCTION 10

REVIEW OF RESEARCH ON OCCLUSION ISSUES IN RGB-D DYNAMIC SCEN...

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Dong, Yuanqiu Wu, Guoxin Zhang, Guolong Zhou, Linglong Qi, Yinyan The Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

As innovation proceeds to advance,3D recreation innovation has ended up a critical portion of the field of computer vision. Among these, 3D reproduction innovation based on RGB-D information sources has ended up a well known approach in later a long time. Right now, the more develop inactive 3D recreation calculations have ended up progressively successful for reproducing stationary objects. Be that as it may, their adequacy lessens when parts of the scene are blocked. This paper briefly presents particular approaches to address impediment issues and the fundamental concepts of energetic 3D remaking innovation based on RGB-D information sources. The points of interest and current investigate status of energetic 3D reproduction focusing on impediment issues utilizing RGB-D information sources are examined. Moreover, the most recent progressions in 3D remaking of energetic scenes are summarized, and proposed future investigate bearings. © The Institution of Engineering & technology 2024.

关键词： 3D reconstruction

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REARCH ON DOUBLE CUBIC INTERPOLATION COMBINED WITH GAUSSIAN FITTING SUB-PIXEL EDGE DETECTION METHOD 10

REARCH ON DOUBLE CUBIC INTERPOLATION COMBINED WITH GAUSSIAN ...

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Qi, Yinyan Wu, Guoxin Li, Yao Dong, Yuanqiu The Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid advancements in intelligent manufacturing technology, the realization of fast and accurate measurements in aerospace, instrumentation, machinery and equipment, automotive electronics, and other sectors has become a research hotspot. Image positioning technology and edge detection methods, with their advantages of being fast, non-contact, and precise, have been widely employed. How to improve the accuracy of image measurement lies in solving the problem of accurate positioning of image edge detection, based on the problem of inaccurate extraction of directional information and large error in contour size of image edge detection technology proposes an improved sub-pixel edge detection algorithm with Gaussian fitting by double cubic interpolation. Based on preprocessing for pixel-level edge feature extraction, pixel-level edge points are acquired utilizing the least squares fitting algorithm. The normal section is determined by computing the normal intercept line, while the normal isometric line is identified through the Gaussian integration method. Sub-pixel fitting points are obtained via boundary fitting, and the final grey value is derived using an edge detection algorithm that combines double cubic interpolation and Gaussian fitting on the fitted points. The experimental results demonstrate that, through comparative testing of this method, it has been verified that it exhibits the highest image quality and is superior to most current algorithms. An improvement in the algorithm by 0.021%, 0.025%, and 0.031% in edge localization accuracy compared to the Zernike moment algorithm, Gaussian fitting algorithm, and bicubic interpolation algorithm has been achieved, effectively verifying that the image quality detected by this method is relatively high. In comparison with the point cloud method regarding fitting error, it was observed that the error index increased by 0.025% under strong burst light conditions, 0.026% under normal burst light conditions, an

关键词： Interpolation

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Research on fault diagnosis of planetary gearbox based on MPGA-BP neural network 8

Research on fault diagnosis of planetary gearbox based on MP...

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8th International Symposium on Test Automation and Instrumentation, ISTAI 2020

作者： Fu, Yong Luan, Zhongquan Zhou, Fuqiang Wang, Shaohong Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781839535635

There are common faults in planetary gearbox that it is not suitable for shutdown detection at the initial stage or there are many kinds of faults which are not easy to classify accurately. Based on the above reasons, this paper proposes a method combining multi population genetic algorithm (MPGA) and BP neural network. Traditional BP neural network uses a variety of genetic algorithms to optimize the initial weights between layers and the initial threshold corresponding to the single layer network. The traditional method greatly increases the global optimization ability of BP neural network when gradient drops, so we avoid the problem that the local optimal of selecting initial weight and initial threshold. This paper uses the tradition BP neural network and optimized MPGA-BP neural network to classify the common faults of planetary gearbox. Then we compare the results of traditional BP neural network and MPGA-BP neural network in planetary gearbox fault classification. The results show that: MPGA-BP neural network has higher prediction accuracy than traditional BP neural network, so this method can be used for fault classification of planetary gearboxes. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Global optimization

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THE TWO-FACTOR DAMAGE THRESHOLD AND PROPROTION OF FRICTION COMPONENT IN WET CLUTCH 9

THE TWO-FACTOR DAMAGE THRESHOLD AND PROPROTION OF FRICTION C...

引用

9th International Symposium on Test Automation and Instrumentation, ISTAI 2022

作者： Chen, Ruihan Li, Le Wang, Liyong Wu, Jianpeng Li, Jin Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781839538391

Frictional thermal load and wear have a complex influence relationships on the wet clutch working performance, and jointly cause damage to the friction ***, the two-factor threshold model of thermal load and wear is constructed using the neural network, and their optimal weights under damage conditions are obtained by training damage threshold data sets. Then, the difference on the weight of two factors is analysed under three specific operating conditions. Finally, the damage causes of wet clutch friction pairs are explained in detail. Research indicates that, the threshold model training error of 3.47 × 10-4is much smaller than the target error of 6.5 × 10-4, which is providing a high accuracy degree. Specifically, wear is the main influence on damage under the heavy load and low speeds conditions. While thermal loading is the main influence on damage under the high speeds and light loads conditions. Both thermal loads and wear have a huge impact on the damage under the high speed and heavy load conditions. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Wear of materials

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APPLICATION OF WAVELET KURTOSIS CONVOLUTIONAL NEURAL NETWORK MODEL IN ROLLING BEARING FAULT DIAGNOSIS 10

APPLICATION OF WAVELET KURTOSIS CONVOLUTIONAL NEURAL NETWORK...

引用

10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Men, Dachao Jiang, Zhanglei Liu, Xiuli Zheng, Wei Li, Zihan Zha, Zhendong Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

Aiming at the problem of substantial noise in the actual industrial environment, where the intelligent diagnostic model exhibits weak anti-noise performance and the interpretability of the extracted features is not strong, the wavelet kurtosis convolutional neural network model integrating signal processing technology and deep learning neural networks has been proposed. Firstly, the standard CNN convolution kernel has been replaced with a wavelet basis function to form a wavelet convolution layer, and the input signal undergoes decomposition into various frequency components via the wavelet filter in this layer;Secondly, a kurtosis loss layer has been used to filter the high kurtosis value components that contain more fault features, so as to achieve the filtration of the noise and the accurate localisation of the fault signals;Lastly, the efficacy of the proposed model has been verified in the rolling bearing dataset and diagnostic model. The results showed that the model can reach 90% diagnostic accuracy under -4db Gaussian white noise interference, which was higher than other diagnostic models, and had good generalisation and noise resistance. © The Institution of Engineering & technology 2024.

关键词： White noise

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HYBRID-DRIVEN MODEL FOR TEMPERATURE PREDICTION IN WET CLUTCHES 10

HYBRID-DRIVEN MODEL FOR TEMPERATURE PREDICTION IN WET CLUTCH...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Huang, Yuanbin Li, Le Wang, Qian Wang, Liyong Key Laboratory of Modern Measurement and Control Technology in Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781837242672

A hybrid-driven model for predicting the temperature at the friction interface of wet clutch friction components was developed, employing a radial basis function neural network algorithm. This model uses temperature data obtained from wet clutch test bench experiments to compensate for the results from a thermo-mechanical coupled numerical simulation model of the friction components. The hybrid model inputs include load pressure, relative speed, radial position of the friction components, and numerically simulated temperatures at the friction interface, to output predicted temperatures under specific operating conditions. The results indicate that the predicted temperatures of the friction components under various conditions align well with experimental values, with a maximum mean relative error of 0.033 and all prediction intervals having a coefficient of determination higher than 0.97. Thus, the hybrid-driven model demonstrates high accuracy and stability in predictions, and possesses strong adaptability and predictive capabilities for different temperature distributions at the friction interface. © The Institution of Engineering & technology 2024.

关键词： Radial basis function networks

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Remote monitoring and control of photovoltaic system using wireless sensor network

Remote monitoring and control of photovoltaic system using w...

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International Conference on Electric Information and control Engineering

作者： Xu, Xiaoli Qiao, Daoe Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781424480395

Efficient energy yield is a major concern in photovoltaic systems in solar energy supply. Smart photovoltaic modules offer a potential solution to allow solar generators to maximize their productivity. This paper describes details of the design and instrumentation of smart photovoltaic modules, a wireless sensor network, and software for real-time sensing and control of a photovoltaic system with maximum power point tracking at module level. Field condition is monitored by voltage, current, irradiance, and temperature sensors distributed across the photovoltaic field. The sensory data are periodically sampled and transmitted to a base station. The power point is controlled by a integrated DC-DC converter. The output voltage in the converter is regulated by a digital controller. The reference voltage is calculated based on a neural network model, which is used to identify maximum power point. The communication data for remote monitoring and distributed control are successfully transmitted using a low-cost ZigBee wireless network. The web-based software developed in this paper offers stable remote access to field conditions and real-time control of the power points in the smart photovoltaic system. © 2011 IEEE.

关键词： Solar energy

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Edge Computing Based Two-Stage Emergency Braking in Autonomous Driving 6th

Edge Computing Based Two-Stage Emergency Braking in Autonomo...

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6th EAI International Conference on IoT as a Service, IoTaaS 2020

作者： Li, Lian Xu, Zhan Chen, Jinhui Zhi, Ruxin Huang, Mingzhe Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Beijing China

ISBN: (纸本)9783030675134

Emergency braking is a key technology in autonomous driving to prevent collision accidents. In this paper, we propose an edge computing based two-stage emergency braking method (TSEB), which can bring passengers more comfortable experience with emergency braking while avoiding collision. In TSEB, the initial deceleration rate is figured out at the first time point, in terms of motion parameters of the moving vehicle and the moving or static obstacle, and used to set the braking force for the first-stage braking, which can at least avoid serious collision. The second deceleration rate is figured out in terms of the parameters at the second time point and implemented on the vehicle by increasing the braking force in the second-stage braking. With the second-stage braking, the vehicle can stop at a safer distance to the obstacle while the passengers do not feel too much deceleration. The performance of TSEB is evaluated on the simulation platform composed of Prescan, Simulink and Matlab. TIS sensors of long distance and short distance are used to detect the distance between the vehicle and the obstacle. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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