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Model predictive control with input disturbance and guaranteed Lyapunov stability for controller approximation

Science China(information Sciences) 2022年第9期65卷 206-218页

作者： Yanye WANG Shaoyuan LI Yi ZHENG Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

An approximation method that can lower the computational complexity is presented to reduce the processing time of model predictive control(MPC). The effects of the inaccurate inputs, which are caused by the approximation errors, are reflected as input disturbances and are considered in the design of the controller. The closed-loop system's stability is guaranteed by a restricted Lyapunov-based constraint and input constraints, ensuring that the states will be eventually bounded at a certain confidence *** to this paper, the effects of the bounded disturbance can be observed via a change in the stability region. The relationship between the regions of the normal and approximated systems is highlighted. The proposed MPC with input disturbance and guaranteed Lyapunov stability is employed in a chemical process,and the simulation results indicate the efficiency of the proposed method.

关键词： model predictive control Lyapunov-based MPC control approximation learning-based control nonlinear system

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Self-Supervised Depth Estimation Based on the Consistency of Synthetic-real Image Prediction 8

Self-Supervised Depth Estimation Based on the Consistency of...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Tong, Wei Gao, Yubing Wu, Edmond Q. Zhu, Li-Min School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu210014 China Ministry of Education Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China

ISBN: (纸本)9798350300178

Learning-based multi-view stereo aims to restore the real scene from multiple images with overlapping areas. The mainstream self-supervised MVS method trains the model based on the assumption that spatial points from different perspectives share the same color information. To further suppress the interference from specular reflection and illumination noise, this work proposes a self-supervised MVS network based on the consistency of synthetic-real image prediction. The network first applies the coarse-to-fine manner to gradually refine the depth map, and the source images are projected to the reference view to generate the synthesized reference image. Then the synthesized image with real source images are re-input into the network to form a cycled network, and the consistency constraint of the prediction results of the two periods is introduced to improve the color anti-interference of the self- supervised MVS model. The comprehensive experiments on the public dataset show that the proposed work can further improve the reconstruction performance of the benchmark model, which verifies the effectiveness of the proposed work. © 2023 IEEE.

关键词： Forecasting

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE International Conference of Intelligent Robotic and control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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Inspection Area Allocation for Forest Surveillance Based on Different Starting Points of Multi-UAVs 7th

Inspection Area Allocation for Forest Surveillance Based on ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhengjun Mu, Lingxia Ning, Kaikai Zhang, Youmin Xue, Xianghong Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819733392

An inspection allocation algorithm based on different starting points and endurance capabilities of unmanned aerial vehicles (UAVs) is proposed for the multi-UAVs forest inspection process. Firstly, the area map to be inspected is rasterized, and the distances between the starting points of each UAV and individual grid cells are computed, which will be used for area decomposition. Subsequently, an evaluation function is employed to allocate individual grid cells reasonably to each UAV. Then, the boundary lines of the allocated sub-regions are generated, by which the raster map is converted into an inspection map with continuous boundaries. This ultimately yields the sub-regions for inspection by each UAV. Simulation results demonstrate that this method effectively allocates the forest inspection area to the various UAVs in a rational manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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Optical techniques in non-destructive detection of wheat quality:A review

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Grain & Oil Science and Technology 2022年第1期5卷 44-57页

作者： Lei Li Si Chen Miaolei Deng Zhendong Gao Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China

Wheat quality detection is essential to ensure the safety ofwheat circulation and *** traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low *** the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality *** methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent *** this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in ***,the principle and process of common optical non-destructive detection methods for wheat quality are ***,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further *** shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and ***,the research of optical technology in wheat quality detection is *** future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat *** studies are of g

关键词： Wheat Quality Optical technology Non-destructive detection

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Blur region detection and segmentation of partial blur image

Blur region detection and segmentation of partial blur image

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2021 International Conference on Signal processing and Communication Technology, SPCT 2021

作者： Wang, Kui-Kui Hou, Yi-Feng Lv, Mei-Ni Gong, Ping Yang, Qiu-Hui Gan, Hui Guangxi Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou543000 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

In order to address the detection and segmentation of partial blur for natural images,a no-reference and training-free algorithm was proposed. Firstly, the test image was re-blurred by a Gaussian low-pass filter. Secondly, the pixel-wise singular value decomposition within N×N neighborhoods for both the test image and the re-blurred image were conducted to obtain the ratio of the end singular value to all singular value. According to the ratio,the blurriness distribution function was structured and the blurriness distribution map was drawed. Finally, combined with the K-means clustering algorithm and the erosion and dilation operations, the test image could be segmented into blur region and clear region. Experiment results show that the proposed approach can effectively detect and segment the partial blur image and behave well at precision, recall and F-score. © 2022 SPIE.

关键词： Distribution functions

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Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch

arXiv

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arXiv 2025年

作者： Wang, Weizhen He, Jianping Duan, Xiaoming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Policy gradient methods are one of the most successful methods for solving challenging reinforcement learning problems. However, despite their empirical successes, many SOTA policy gradient algorithms for discounted problems deviate from the theoretical policy gradient theorem due to the existence of a distribution mismatch. In this work, we analyze the impact of this mismatch on the policy gradient methods. Specifically, we first show that in the case of tabular parameterizations, the methods under the mismatch remain globally optimal. Then, we extend this analysis to more general parameterizations by leveraging the theory of biased stochastic gradient descent. Our findings offer new insights into the robustness of policy gradient methods as well as the gap between theoretical foundations and practical implementations. © 2025, CC BY.

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Grain Depot Wearable Device Detection Model Based on YOLOv5

Grain Depot Wearable Device Detection Model Based on YOLOv5

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2023 International Conference on Intelligent Management and Software Engineering, IMSE 2023

作者： Wu, Jianjun Lian, Yimeng Wen, Qing Zhen, Tong Zhu, Yuhua Li, Zhihui College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9798350394146

Standardized wearing of helmets and ventilators is the focus of grain depot safety management, which can effectively prevent safety accidents such as falling objects hurting people and fumigation poisoning. This paper is based on the deep learning algorithm to detect the wearing specifications of grain depot operations. Firstly, collect images by simulating the operation scene of the grain depot, recording video frames on the spot, etc. Use Labellmag software to mark the personnel wearing and not wearing the device, and create the original data set;Then, a grain depot wear standard monitoring model based on YOLOv5 is constructed. Add CBAM Attention to improve the network's attention to local and global features and the ability to extract small target location information. In the post-processing stage, DIOU-NMS is optimized as the screening scheme of the target boxes to reduce the rate of missed detection rate of adjacent targets. Through the contrast experiment, the results show that the improved model mAP(mean average precision) is increased from 92.6% to 93.9%, and finally the detection model meeting the detection accuracy and real-time requirements is obtained. ©2023 IEEE.

关键词： Object detection

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Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

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Chinese Journal of Chemical Engineering 2023年第4期56卷 46-57页

作者： Haoshan Duan Xi Meng Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing 100124China

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词： Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model

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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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