Pub Date : 2018-05-18DOI: 10.1109/YAC.2018.8406350
Yunxi Zhang, Jia Liu, Wenyu Lian, Lingbo Yang
Aiming at the related problems of discrete guidance law, the design of the dynamic delay characteristic of missile autopilots is considered as discrete finite time convergent sliding mode guidance law for the first-order inertial link. Firstly, the discrete guidance equation considering the dynamic characteristics of autopilot was derived; secondly, the discrete sliding mode variable structure guidance law was analyzed; thirdly, the finite time convergence characteristic of the designed guidance law was analyzed and proved. The simulation results show that the designed discrete sliding mode guidance law is convergent in finite time, which ensures the stability of guidance and improves the precision of guidance.
{"title":"Discrete sliding mode guidance law considering dynamic characteristics for autopilot","authors":"Yunxi Zhang, Jia Liu, Wenyu Lian, Lingbo Yang","doi":"10.1109/YAC.2018.8406350","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406350","url":null,"abstract":"Aiming at the related problems of discrete guidance law, the design of the dynamic delay characteristic of missile autopilots is considered as discrete finite time convergent sliding mode guidance law for the first-order inertial link. Firstly, the discrete guidance equation considering the dynamic characteristics of autopilot was derived; secondly, the discrete sliding mode variable structure guidance law was analyzed; thirdly, the finite time convergence characteristic of the designed guidance law was analyzed and proved. The simulation results show that the designed discrete sliding mode guidance law is convergent in finite time, which ensures the stability of guidance and improves the precision of guidance.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131767241","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406346
Yonghao Ma, Zhijia Zhao, Xuejing Lan, Xiuyu He, Wei He
In this paper, we address the vibration control and angle positioning problem of a distributed parameter flexible Timoshenko arm system in the presence of input saturation constraint. The smooth hyperbolic tangent function is adopted to develop boundary controls for regulating the vibration and shear deformation, achieving the angle tracking and restricting the control input in the specified area. The proposed controls are able to make sure that the robotic manipulator is placed in the desired angular. Finally, simulation results are demonstrated for control performance verification.
{"title":"Boundary constrained control of a flexible Timoshenko arm","authors":"Yonghao Ma, Zhijia Zhao, Xuejing Lan, Xiuyu He, Wei He","doi":"10.1109/YAC.2018.8406346","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406346","url":null,"abstract":"In this paper, we address the vibration control and angle positioning problem of a distributed parameter flexible Timoshenko arm system in the presence of input saturation constraint. The smooth hyperbolic tangent function is adopted to develop boundary controls for regulating the vibration and shear deformation, achieving the angle tracking and restricting the control input in the specified area. The proposed controls are able to make sure that the robotic manipulator is placed in the desired angular. Finally, simulation results are demonstrated for control performance verification.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125298331","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}
Sparse auto encoder(SAE) can reduces information loss and extract the meaningful feature by learning the deep structure of complex data. This paper presents a novel SAE based semi-supervised feature learning method for fault diagnosis of batch process which includes two stages, namely, unsupervised pre-training stage and supervised tuning stage. At the unsupervised pre-training stage, denoising SAE(DSAE) is utilized by introducing denoising auto encoder into SAE to improve the robustness of network. At the supervised tuning stage, the pretrained DSAE netwrok is optimized using back propagation algorithm to improve the accuracy of classification. The proposed method is validated on penicillin fermentation simulation experiment and Escherichia coli fermentation experiment. Experimental results show that the proposed approach achieves good fault diagnostic performance and is superirior to the traditional fault diagnosis method.
{"title":"Fault diagnosis of batch process based on denoising sparse auto encoder","authors":"Xuejin Gao, Hao Wang, Huihui Gao, Xichang Wang, Zidong Xu","doi":"10.1109/YAC.2018.8406474","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406474","url":null,"abstract":"Sparse auto encoder(SAE) can reduces information loss and extract the meaningful feature by learning the deep structure of complex data. This paper presents a novel SAE based semi-supervised feature learning method for fault diagnosis of batch process which includes two stages, namely, unsupervised pre-training stage and supervised tuning stage. At the unsupervised pre-training stage, denoising SAE(DSAE) is utilized by introducing denoising auto encoder into SAE to improve the robustness of network. At the supervised tuning stage, the pretrained DSAE netwrok is optimized using back propagation algorithm to improve the accuracy of classification. The proposed method is validated on penicillin fermentation simulation experiment and Escherichia coli fermentation experiment. Experimental results show that the proposed approach achieves good fault diagnostic performance and is superirior to the traditional fault diagnosis method.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129606080","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406416
Hao Lei, Liang Xu, Boyi Chen, Yuping Lu, Mingmin Sun
This paper deals with the model reduction methods for very flexible aircraft (VFA). Such aircraft is characterized by strong coupling between rigid motion and flexible motion, resulting in a very high order system for controll design. Recently released high-fidelity Vulture model of 707 order is chosen to be a typical example of VFA and is regarded as the reference model. Then three model reduction methods: balanced truncation method, balanced residual method and optimal hankel norm approximation are presented. Finally, numerical simulation is used to illustrate the characteristic of VFA and determine the best method for model reduction of VFA. In addition, the relationship between model error and model order is given. Both frequency domain and time domain analysis verify the reduced model of order 80 using balanced singular perturbation method is sufficient to maintain the accuracy of the full model.
{"title":"Comparison of model reduction methods for very flexible aircraft","authors":"Hao Lei, Liang Xu, Boyi Chen, Yuping Lu, Mingmin Sun","doi":"10.1109/YAC.2018.8406416","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406416","url":null,"abstract":"This paper deals with the model reduction methods for very flexible aircraft (VFA). Such aircraft is characterized by strong coupling between rigid motion and flexible motion, resulting in a very high order system for controll design. Recently released high-fidelity Vulture model of 707 order is chosen to be a typical example of VFA and is regarded as the reference model. Then three model reduction methods: balanced truncation method, balanced residual method and optimal hankel norm approximation are presented. Finally, numerical simulation is used to illustrate the characteristic of VFA and determine the best method for model reduction of VFA. In addition, the relationship between model error and model order is given. Both frequency domain and time domain analysis verify the reduced model of order 80 using balanced singular perturbation method is sufficient to maintain the accuracy of the full model.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125916388","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406498
Jiaxi Lin, Xinde Li, Hong Pan
Object recognition is one of the fundamental issues in the field of computer vision. In traditional methods, invariant features are extracted from segmented targets for recognition. However, there is no common method for segmentation of aircraft targets so far due to the complex backgrounds, illuminations, noise and other practical factors. Therefore, in this paper, we propose a method for aircraft identification in remote sensing images based on HOG and deep learning features. We train two classifiers, one is the SVM classifier based on HOG feature, and the other is a classifier based on deep convolutional neural network VGGNet. First, we use the SVM classifier to identify the aircraft in the picture roughly, then we use the deep learning classifier to exclude misidentified targets. In this way, this coarse to fine framework can significantly improve the speed and accuracy of aircraft recognition in remote sensing images. At the same time, our method has a better generalization capability than the traditional methods. Experimental results demonstrate the robustness of our method.
{"title":"Aircraft recognition in remote sensing images based on deep learning","authors":"Jiaxi Lin, Xinde Li, Hong Pan","doi":"10.1109/YAC.2018.8406498","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406498","url":null,"abstract":"Object recognition is one of the fundamental issues in the field of computer vision. In traditional methods, invariant features are extracted from segmented targets for recognition. However, there is no common method for segmentation of aircraft targets so far due to the complex backgrounds, illuminations, noise and other practical factors. Therefore, in this paper, we propose a method for aircraft identification in remote sensing images based on HOG and deep learning features. We train two classifiers, one is the SVM classifier based on HOG feature, and the other is a classifier based on deep convolutional neural network VGGNet. First, we use the SVM classifier to identify the aircraft in the picture roughly, then we use the deep learning classifier to exclude misidentified targets. In this way, this coarse to fine framework can significantly improve the speed and accuracy of aircraft recognition in remote sensing images. At the same time, our method has a better generalization capability than the traditional methods. Experimental results demonstrate the robustness of our method.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132818476","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}
The outputs of adjacent wind farms have some correlation characteristics because of similar resource conditions, which has certain influences on transmission network expansion planning. In this paper, the influences of multi-wind power output correlations on transmission network expansion planning are considered. Firstly, the correlations between the outputs of adjacent wind farms are studied by Copula theory, and the method of generating wind farm samples with correlation is presented. Secondly, a transmission network expansion planning model is constructed based on the minimum cost method. In this model, Monte Carlo simulation technology is utilized to analyze uncertain characteristics of power flows resulted from multi-wind farms that are integrated into electric power grids simultaneously. At last, the simulation results based on the improved Garver 6 system verify the feasibility and rationality of the proposed model and algorithm. The results also show that the correlations of wind power outputs among multi-wind farm have a significant effect on the transmission network planning results, which should be taken into account in the planning.
{"title":"Transmission network expansion planning considering multi-wind power output correlations","authors":"Shuxiu Cao, Hui Zhou, Xinsong Zhang, Juping Gu, Liang Hua, Jiale Wang","doi":"10.1109/YAC.2018.8406482","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406482","url":null,"abstract":"The outputs of adjacent wind farms have some correlation characteristics because of similar resource conditions, which has certain influences on transmission network expansion planning. In this paper, the influences of multi-wind power output correlations on transmission network expansion planning are considered. Firstly, the correlations between the outputs of adjacent wind farms are studied by Copula theory, and the method of generating wind farm samples with correlation is presented. Secondly, a transmission network expansion planning model is constructed based on the minimum cost method. In this model, Monte Carlo simulation technology is utilized to analyze uncertain characteristics of power flows resulted from multi-wind farms that are integrated into electric power grids simultaneously. At last, the simulation results based on the improved Garver 6 system verify the feasibility and rationality of the proposed model and algorithm. The results also show that the correlations of wind power outputs among multi-wind farm have a significant effect on the transmission network planning results, which should be taken into account in the planning.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122988677","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406523
N. Xie, Xinle Li, Yao Yu
Position estimation based on vision system is essential for a UAV in the GPS-deny situation to realize the position control. However, in the practice, the hardware and software are not ideal for the UAV to carry when flying. Thus, this study is aiming at the problem of position estimation and control for a quadrotor UAV based on vision. Firstly, a position estimation algorithm based on vision is proposed, in which optical flow is used combining with FAST corner detection to ensure the real-time performance of the estimation in condition with limited loader. Meanwhile, a robust controller with nonlinear compensating input has been designed in this paper to deal with the position control for the quadrotor. A simulation is presented in this paper to verify the performance of the controller. Moreover, an experiment is also utilized in this paper to show that the method is practical and with high performance.
{"title":"A position estimation and control system for the quadrotor in GPS-deny situation based on FAST detection and optical flow","authors":"N. Xie, Xinle Li, Yao Yu","doi":"10.1109/YAC.2018.8406523","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406523","url":null,"abstract":"Position estimation based on vision system is essential for a UAV in the GPS-deny situation to realize the position control. However, in the practice, the hardware and software are not ideal for the UAV to carry when flying. Thus, this study is aiming at the problem of position estimation and control for a quadrotor UAV based on vision. Firstly, a position estimation algorithm based on vision is proposed, in which optical flow is used combining with FAST corner detection to ensure the real-time performance of the estimation in condition with limited loader. Meanwhile, a robust controller with nonlinear compensating input has been designed in this paper to deal with the position control for the quadrotor. A simulation is presented in this paper to verify the performance of the controller. Moreover, an experiment is also utilized in this paper to show that the method is practical and with high performance.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122200504","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406363
Yongliang Yang, Dawei Ding, Yixin Yin, D. Wunsch
In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.
{"title":"Model-free semi-global output regulation for discrete-time linear systems subject to input amplitude saturation","authors":"Yongliang Yang, Dawei Ding, Yixin Yin, D. Wunsch","doi":"10.1109/YAC.2018.8406363","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406363","url":null,"abstract":"In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126061484","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406423
Chen Gao, Xiao He
We study the fault-tolerant consensus problem for a class of general linear multi-agent systems subject to actuator saturation and faults. We design a distributed control protocol using the state information of neighbours to compensate the actuator fault and analyse the domain of attraction in the presence of the saturation nonlinearity. An optimization problem is formulated based on the linear matrix inequality (LMI) approach to figure out the largest domain of attraction. Finally, we conduct the simulation example to illustrate the theoretical results.
{"title":"Fault-tolerant consensus control for multi-agent systems with actuator saturation","authors":"Chen Gao, Xiao He","doi":"10.1109/YAC.2018.8406423","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406423","url":null,"abstract":"We study the fault-tolerant consensus problem for a class of general linear multi-agent systems subject to actuator saturation and faults. We design a distributed control protocol using the state information of neighbours to compensate the actuator fault and analyse the domain of attraction in the presence of the saturation nonlinearity. An optimization problem is formulated based on the linear matrix inequality (LMI) approach to figure out the largest domain of attraction. Finally, we conduct the simulation example to illustrate the theoretical results.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133094916","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 : 2018-05-18DOI: 10.1109/YAC.2018.8406355
Zhenyu Lu, You Fu, Yunan Qiu, Bingjian Lu
The traditional two-Dimensional Principal Component Analysis(2DPCA) only extracts the in-line features of data of face image, the direction of feature extraction is relatively simple, and the feature extraction in other directions is not considered. In order to extract the features of the image from multiple angles and provide more abundant information for recognition, a new method of 2DPCA face recognition is proposed. The new algorithm first self-corrects the face image, at the same time, it extracts the low frequency information of the image, and then it uses the Perceptual hash technique to obtain the ‘fingerprint’ of the image. Then, the new algorithm will rotate multi-angle images from the self-corrected face images and extract the features separately to get multi-angle feature information. Finally, the training sample pictures are classified again for each category, and the images of similar expressions or features are classified to retain the special expressions or features. The numerical experiments in the ORL human face databases show that the improved algorithm is superior to the traditional 2DPCA algorithm.
{"title":"A new algorithm of improved two-dimensional principal component analysis face recognition","authors":"Zhenyu Lu, You Fu, Yunan Qiu, Bingjian Lu","doi":"10.1109/YAC.2018.8406355","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406355","url":null,"abstract":"The traditional two-Dimensional Principal Component Analysis(2DPCA) only extracts the in-line features of data of face image, the direction of feature extraction is relatively simple, and the feature extraction in other directions is not considered. In order to extract the features of the image from multiple angles and provide more abundant information for recognition, a new method of 2DPCA face recognition is proposed. The new algorithm first self-corrects the face image, at the same time, it extracts the low frequency information of the image, and then it uses the Perceptual hash technique to obtain the ‘fingerprint’ of the image. Then, the new algorithm will rotate multi-angle images from the self-corrected face images and extract the features separately to get multi-angle feature information. Finally, the training sample pictures are classified again for each category, and the images of similar expressions or features are classified to retain the special expressions or features. The numerical experiments in the ORL human face databases show that the improved algorithm is superior to the traditional 2DPCA algorithm.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137598","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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