Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722000
Wei Liu, Mou Chen
In this study, a nonlinear anti-swing controller based on energy shaping and disturbance observer is designed for the longitudinal system model of unmanned helicopter slung-load under the external disturbance. A finite time sliding mode disturbance observer (FTSMDO) is employed to estimate the external disturbance, and the anti-swing controller is devised by combining with the energy shaping and Lyapunov analysis. The Lyapunov principle and LaSalle invariance theorem are used to prove the asymptotic stability of all errors in the closed-loop system, and the effectiveness of the proposed method is verified by a comparative simulation.
{"title":"Nonlinear Control with Energy Shaping for Unmanned Helicopter Slung-load System Based on Disturbance Observer","authors":"Wei Liu, Mou Chen","doi":"10.1109/ICCSS53909.2021.9722000","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722000","url":null,"abstract":"In this study, a nonlinear anti-swing controller based on energy shaping and disturbance observer is designed for the longitudinal system model of unmanned helicopter slung-load under the external disturbance. A finite time sliding mode disturbance observer (FTSMDO) is employed to estimate the external disturbance, and the anti-swing controller is devised by combining with the energy shaping and Lyapunov analysis. The Lyapunov principle and LaSalle invariance theorem are used to prove the asymptotic stability of all errors in the closed-loop system, and the effectiveness of the proposed method is verified by a comparative simulation.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127175718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721994
Shimin Zhang, Jiangsheng Gui, Zhihui Cai
Magnetic multilayer materials are extensively used in micro-devices and nanoelectronics areas. It is significant to implement edge detection and extraction for the microstructure images of the multilayer materials. This research deals with the edge detection and extraction of microstructures images of the magnetic multilayer material based on a richer convolutional features (RCF) network. First, an RCF network model on a 20-fold expanded Berkeley Segmentation Data Set and benchmark 500 (BSDS500) dataset is retrained. Then, such model is applied to the edge detection test on the given microstructure images of the magnetic multilayer material, and the edge probability maps containing coarse and obvious boundaries between the layers of magnetic materials are obtained. Third, the non-maximum suppression (NMS) algorithm is introduced to further refine the thick edges of the microstructure images. The results demonstrate that the RCF-based edge detection method is capable of detecting light and unclear boundaries of the magnetic multilayer material from their images, and outperforms the existing other edge detection algorithms includes Canny operator and HED network. In addition, under the expanded RCF model combining with the NMS algorithm, the edge probability map of the microstructure images of the magnetic multilayer material are almost the same as the ground truth.
{"title":"Edge Detection of Microstructure Images of Magnetic Multilayer Materials via a Richer Convolutional Features Network","authors":"Shimin Zhang, Jiangsheng Gui, Zhihui Cai","doi":"10.1109/ICCSS53909.2021.9721994","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721994","url":null,"abstract":"Magnetic multilayer materials are extensively used in micro-devices and nanoelectronics areas. It is significant to implement edge detection and extraction for the microstructure images of the multilayer materials. This research deals with the edge detection and extraction of microstructures images of the magnetic multilayer material based on a richer convolutional features (RCF) network. First, an RCF network model on a 20-fold expanded Berkeley Segmentation Data Set and benchmark 500 (BSDS500) dataset is retrained. Then, such model is applied to the edge detection test on the given microstructure images of the magnetic multilayer material, and the edge probability maps containing coarse and obvious boundaries between the layers of magnetic materials are obtained. Third, the non-maximum suppression (NMS) algorithm is introduced to further refine the thick edges of the microstructure images. The results demonstrate that the RCF-based edge detection method is capable of detecting light and unclear boundaries of the magnetic multilayer material from their images, and outperforms the existing other edge detection algorithms includes Canny operator and HED network. In addition, under the expanded RCF model combining with the NMS algorithm, the edge probability map of the microstructure images of the magnetic multilayer material are almost the same as the ground truth.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121636560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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