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Discrete-time sliding mode control for a class of stochastic system

ICIC Express Letters 2013年第7期7卷 2087-2092页

作者： Chang, Ru Fang, Yiming Liu, Le Li, Jianxiong Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University No. 438 Hebei Street Qinhuangdao 066004 China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Hebei Street Qinhuangdao 066004 China

This paper is concerned with the discrete-time sliding mode control (SMC) problem for a class of stochastic system. A sufficient condition is presented to ensure the mean-square asymptotic stability of the stochastic system in the sliding mode by solving a linear objective function. Moreover, a power function is introduced to design a novel discrete sliding mode controller. The controller can guarantee the good dynamic quality of the stochastic system, and can ensure the reachability of the discrete-time sliding motion. Finally, a numerical example is given to illustrate the validity of the proposed SMC scheme. © 2013 ICIC International.

关键词： Stochastic systems

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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly lab.led and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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A single-station location method based on long baseline interferometer

A single-station location method based on long baseline inte...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Li Ping Wang Guohong Xiaopeng Yan Gong Peng Wenbo Shi Liu Jian Beijing Institute of Technology National Key Lab. of Mechatronic Engineering and Control Beijing China Northeastern University School of Computer and Communication Engineering Qinhuangdao China China Electronics Technology Group Corporation Zhejiang China

ISBN: (纸本)9781479944156

Conventional long baseline interferometer based Direction of Arrival (DOA) requires multi-group long baselines and short baselines to deblur. In this paper, only using the time difference of arrival (TDOA) data of long baseline interferometer can cancel the debluring shortage and achieve high-precision DOA estimation. Meanwhile iterative filtering algorithm is adopted to extract high-precision changing rate of phase difference from the fuzzy phase difference data of long baseline. Combined with the measured DF and the change rate of phase difference, particle filter algorithm is utilized to achieve high-precision single-station passive location. Parameter measurement and the distribution of location error are verified through theoretical simulations. And the simulation results demonstrate the efficiency of the single-station location method based on long baseline interferometer.

关键词： Direction-of-arrival estimation Accuracy Filtering Atmospheric measurements Particle measurements

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Cooperative and Active Sensing in Mobile Sensor Networks for Scalar Field Mapping

Cooperative and Active Sensing in Mobile Sensor Networks for...

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IEEE International Conference on Automation Science and engineering

作者： Hung Manh La Weihua Sheng Jiming Chen Center for Advanced Infrastructure and Transportation (CAIT) Rutgers University Piscataway NJ 08854 USA School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 USA State Key Lab. of Industrial Control Technology Department of Control Zhejiang University Hangzhou P.R.China 310027

ISBN: (纸本)9781479915132

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each location. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are taken and fused with the corresponding confidences using distributed consensus filters. As a result an online map of the scalar field is built with a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms

关键词： Active sensing Sensor fusion Sensor networks

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Deep Learning based Defect Detection Algorithm for Solar Panels 5

Deep Learning based Defect Detection Algorithm for Solar Pan...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Li, Jiaqi Li, Hongxu Wu, Yifan Zhou, Hailiang Manfredi, Luigi Li, Peng Zhang, Hong Hebei Province Yanshan University Department of Key Lab of Industrial Computer Control Engineering Qinhuangdao066004 China University of Alberta Department of Computing Science EdmontonABT6G 2R3 Canada Hebei Baoding Jiasheng Photovoltaic Technology Co. Ltd. China United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350307320

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly availab.e solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots. © 2023 IEEE.

关键词： Solar panels

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Optimal power allocation for heterogeneous data traffic in NAF relay-assisted uplink transmission

Optimal power allocation for heterogeneous data traffic in N...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Peng Gong Haiqiao Wu Ping Li Wenbo Shi Duk Kyung Kim Beijing Institute of Technology National Key Lab. of Mechatronic Engineering and Control Beijing China Northeastern University School of Computer and Communication Engineering Qinhuangdao China Department of Information and Communication Engineering Inha University Nam-gu Incheon Korea

ISBN: (纸本)9781479944156

For the future wireless networks, heterogeneous data traffic services, i.e., variant bit-rate (VBR) service and constant bit rate (CBR) service are needed to be supported. The optimal power allocation algorithm for the non-orthogonal AF (NAF) Relays-assisted multicast transmission, which may be suitable for the VBR service, has been investigated in [2]. However, the power allocation algorithms for the cases of CBR service and heterogeneous data traffics service are not considered. In this paper, a power allocation problem is addressed by considering the heterogeneous data traffic with VBR and CBR services in the uplink data transmission, where Different from assumption of infinite length of spreading code in [2], a finite length of spreading code is considered and the interference among the signals from mobile terminal and relays exists. And then, an optimal power allocation algorithm is proposed by solving its inverse problem. Simulation results demonstrate the efficiency of the proposed algorithm.

关键词： Relays Mobile communication Algorithm design and analysis Three-dimensional displays Resource management Power demand Radio access networks

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Multi-objective optimal control algorithm for HVAC based on particle swarm optimization

Multi-objective optimal control algorithm for HVAC based on ...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yanyu Zhang Peng Zeng Chuanzhi Zang School of Computer and Information Engineering Henan University Kaifeng China University of Chinese Academy of Sciences Beijing China Key Lab. of Networked Control System Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781479936519

Residential sector is the biggest potential field of reducing peak demand through demand response (DR) in smart grid. Heating, ventilating, and air conditioning (HVAC) is the largest residential electricity user in house. Therefore, controlling the operation of HVAC is an effective method to implement DR in residential sector. The algorithms proposed in literature are single objective optimization algorithms that only minimize the electricity cost and could not quantify the user's comfort level. To tackle this problem, this paper proposes a comfort level indicator, builds a multi-objective scheduling model, and presents a multi-objective optimal control algorithm for HVAC based on particle swarm optimization (PSO). The algorithm controls the operation of HVAC according to electricity price, outdoor temperature forecast, and user preferences to minimize the electricity cost and maximize the user comfort level simultaneously. The proposed algorithm is verified by simulations, and the results demonstrate that it can decrease the electricity cost significantly and maintain the user comfort level effectively.

关键词： Electricity Vectors Smart grids Pareto optimization Optimal control Temperature control

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Adaptive backstepping control of multi-model switching for the hydraulic servo position system of a rolling mill

Beijing Keji Daxue Xuebao/Journal of University of Science a...

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Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing 2012年第11期34卷 1346-1351页

作者： Shao, Peng-Zhen Fang, Yi-Ming Wang, Wen-Bin Jiao, Zong-Xia Hebei Key Lab. of Industrial Computer Control Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

To deal with the structure jumping problem resulting from elastic load force and external load force jumping in different working conditions, an adaptive backstepping control method of multi-model switching was presented based on the common Lyapunov function method and the adaptive backstepping method. This approach combines the features of adaptive backstepping control and the design requirements of a common Lyapunov function, and the adaptive controller for every sub-system and the parameter adaptive estimators for the uncertainty upper bound are designed by backstepping control, then taking the Lyapunov function designed by backstepping as a common Lyapunov function to ensure that the system is asymptotically stable under arbitrary switching. It is easy to select the common Lyapunov function by the combination of backstepping and the common Lyapunov function method, and the problem of parameter jumping and parameter slow time-varying existing at the same time is solved. Simulation results demonstrate that the designed controllers can ensure not only good dynamic and static performance for the hydraulic servo position system of a rolling mill, but also good robustness for the system parameter jumping and parameter slow time-varying.

关键词： Backstepping

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Reach-Avoid Analysis for Delay Differential Equations

Reach-Avoid Analysis for Delay Differential Equations

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IEEE Conference on Decision and control

作者： Bai Xue Yunjun Bai Naijun Zhan Wenyou Liu Li Jiao State Key Lab. of Computer Science Institute of Software CAS Univ. of Chinese Academy of Sciences Beijing China China Academy of Space Technology Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9781665436601

Time-delay systems are ubiquitous in nature and occur in connection with various aspects of physical, chemical, biological and economic systems. In this paper we propose a semi-definite programming method to address reach-avoid problems for time-delay systems modeled by polynomial delay differential equations (DDEs). The reach-avoid problem of interest is to compute an inner-approximation (i.e., sub-set) of a reach-avoid set, which is the set of initial functions enabling the time-delay system to eventually enter a desirable target set while remaining inside a specified safe set till the target hit. In our approach we first derive an estimate of discrepancies between current states and delayed ones for the time-delay system, and then propose a semi-definite program for inner-approximating the reach-avoid set via incorporating the discrepancy estimate. The incorporation of discrepancy estimates facilitates the reduction of conservativeness in computing inner-approximations. Finally, three examples with comparisons are presented to demonstrate the performance of our approach.

关键词： Economics Law Computational modeling Conferences Differential equations Programming Mathematical models

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FedSiam-DA: Dual-aggregated Federated Learning via Siamese Networks under Non-IID Data

arXiv

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arXiv 2022年

作者： Yang, Ming Wang, Yanhan Wang, Xin Zhang, Zhenyong Wu, Xiaoming Cheng, Peng Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou310027 China

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated learning approach, to personalize both local and global models, under various settings of data heterogeneity. Firstly, based on the idea of contrastive learning in the Siamese Network, FedSiam-DA regards the local and global model as different branches of the Siamese Network during the local training and controls the update direction of the model by constantly changing model similarity to personalize the local model. Secondly, FedSiam-DA introduces dynamic weights based on model similarity for each local model and exercises the dual-aggregated mechanism to further improve the generalization of the global model. Moreover, we provide extensive experiments on benchmark datasets, the results demonstrate that FedSiam-DA achieves outperforming several previous FL approaches on heterogeneous datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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