Pub Date : 2017-05-01DOI: 10.1109/DDCLS.2017.8068140
Darong Huang, Dongjie Zhao, Ling Zhao
In this paper, the optimal route and deployment scheme are designed to ensure the shortest retention time for unmanned aerial vehicles (UAV) in risk area. Firstly, according to the known data and radar scanning range, the regional distribution map of target grope and base are obtained, respectively. Secondly, based on the different scanning bandwidth of loads, target points are classified by using clustering analysis. This makes the target points fall on the scanning bandwidth of UAV as far as possible, accordingly reducing the UAV's scanning times. This problem can be regarded as a travelling salesman problem in radar scanning range. Finally, the deployment result and locally optimal route are obtained by 0–1 programming in LINGO. Furthermore, particle swarm optimization is used to improve the local optimal path and the global optimal route can then be generated.
{"title":"A new method of the shortest path planning for unmanned aerial vehicles","authors":"Darong Huang, Dongjie Zhao, Ling Zhao","doi":"10.1109/DDCLS.2017.8068140","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068140","url":null,"abstract":"In this paper, the optimal route and deployment scheme are designed to ensure the shortest retention time for unmanned aerial vehicles (UAV) in risk area. Firstly, according to the known data and radar scanning range, the regional distribution map of target grope and base are obtained, respectively. Secondly, based on the different scanning bandwidth of loads, target points are classified by using clustering analysis. This makes the target points fall on the scanning bandwidth of UAV as far as possible, accordingly reducing the UAV's scanning times. This problem can be regarded as a travelling salesman problem in radar scanning range. Finally, the deployment result and locally optimal route are obtained by 0–1 programming in LINGO. Furthermore, particle swarm optimization is used to improve the local optimal path and the global optimal route can then be generated.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126880182","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068158
Junyuan Tang, Jun Wu, Shengjun Huang
Maglev train is a new urban transportation tool. Track is considered as a component of maglev transportation system and it would directly affect the safety of train operation. An algorithm based on triple threshold filtering is put forward to conduct detection of maglev track, which can be used to filter abnormal points to judge the vertical suspected irregularity of track. In the scheme, the threshold settings for gap difference, current change rate and acceleration differences on suspended controller are set according to characteristics of low sampling rate, high data repeatability and large data volume of automobile data recorder, it can extract information of track irregularity. In order to improve the reliability of the algorithm, 20 sets of data from 5 independent bogies of two trains are clustered for analysis, and get the suspected area of vertical irregularity. The data of maglev train of Changsha operating railway was tested finally.
{"title":"On middle and low speed maglev track irregularity detection based on threshold filtering algorithm","authors":"Junyuan Tang, Jun Wu, Shengjun Huang","doi":"10.1109/DDCLS.2017.8068158","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068158","url":null,"abstract":"Maglev train is a new urban transportation tool. Track is considered as a component of maglev transportation system and it would directly affect the safety of train operation. An algorithm based on triple threshold filtering is put forward to conduct detection of maglev track, which can be used to filter abnormal points to judge the vertical suspected irregularity of track. In the scheme, the threshold settings for gap difference, current change rate and acceleration differences on suspended controller are set according to characteristics of low sampling rate, high data repeatability and large data volume of automobile data recorder, it can extract information of track irregularity. In order to improve the reliability of the algorithm, 20 sets of data from 5 independent bogies of two trains are clustered for analysis, and get the suspected area of vertical irregularity. The data of maglev train of Changsha operating railway was tested finally.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126392814","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068169
Wenkang Guan, Huijin Fan, Li Xu, Yongji Wang
Greedy algorithm is powerful and practical and has been used frequently in compressed sensing because it leads to relatively small calculation and is easy to be realized. GraDeS (Gradient Descent with Sparsification) is one of the greedy algorithms, which reconstructs signal by gradient iteration with hard threshold. However the sparsity of original signal is necessary in GraDes which means it is only applicable to signals with known sparsity, but it is normally unreal in practice. This paper proposes an adaptive gradient greedy algorithm(AGraDeS) in which sparsity of signal is no more required. Experimental results show that the proposed algorithm reconstructs signal faster and precisely in most cases compared to some traditional algorithms, especially when the signal is big and with bad sparsity, this algorithm still performs better.
贪心算法功能强大，实用性强，计算量相对较小，易于实现，在压缩感知中得到了广泛的应用。梯度下降(Gradient Descent with Sparsification)是一种贪婪算法，它通过带硬阈值的梯度迭代重构信号。然而，原始信号的稀疏性在等级中是必要的，这意味着它只适用于已知稀疏性的信号，但在实践中通常是不真实的。本文提出了一种不需要信号稀疏性的自适应梯度贪婪算法。实验结果表明，与传统算法相比，该算法在大多数情况下都能更快、更精确地重建信号，特别是在信号较大且稀疏度较差的情况下，该算法仍然具有更好的性能。
{"title":"An adaptive gradient greedy algorithm for compressed sensing","authors":"Wenkang Guan, Huijin Fan, Li Xu, Yongji Wang","doi":"10.1109/DDCLS.2017.8068169","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068169","url":null,"abstract":"Greedy algorithm is powerful and practical and has been used frequently in compressed sensing because it leads to relatively small calculation and is easy to be realized. GraDeS (Gradient Descent with Sparsification) is one of the greedy algorithms, which reconstructs signal by gradient iteration with hard threshold. However the sparsity of original signal is necessary in GraDes which means it is only applicable to signals with known sparsity, but it is normally unreal in practice. This paper proposes an adaptive gradient greedy algorithm(AGraDeS) in which sparsity of signal is no more required. Experimental results show that the proposed algorithm reconstructs signal faster and precisely in most cases compared to some traditional algorithms, especially when the signal is big and with bad sparsity, this algorithm still performs better.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134252883","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068104
Chiang-Ju Chien, Ying-Chung Wang, M. Er, R. Chi, D. Shen
In this paper, we present a new adaptive iterative learning controller for a class of discrete-time nonlinear systems with iteration-varying uncertainties including initial tracking error, system parameters and external disturbance. The learning objective is to control the nonlinear system to track an iteration-varying desired trajectory after suitable numbers of learning iterations. The main challenge for the iterative learning control design is that all the system parameters are iteration-varying. After separating the system parameters into a pure time-varying component and an iteration-varying component, the system dynamics are divided into an iteration-independent nominal part and an iteration-dependent uncertain part. An adaptive iterative learning controller is then designed to control the nominal dynamics and an iteration-varying boundary layer with dead-zone like auxiliary error is proposed to compensate for the iteration-varying uncertainties. The control parameters and the width of boundary layer are updated from trial to trial in order to guarantee the stability and convergence of the learning system. In addition to ensure the boundedness of control signals for each iteration and each time instant, we also prove that the norm of output error will asymptotically converge to a residual set whose size depends on the width of boundary layer as iteration number goes to infinity.
{"title":"An adaptive iterative learning control for discrete-time nonlinear systems with iteration-varying uncertainties","authors":"Chiang-Ju Chien, Ying-Chung Wang, M. Er, R. Chi, D. Shen","doi":"10.1109/DDCLS.2017.8068104","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068104","url":null,"abstract":"In this paper, we present a new adaptive iterative learning controller for a class of discrete-time nonlinear systems with iteration-varying uncertainties including initial tracking error, system parameters and external disturbance. The learning objective is to control the nonlinear system to track an iteration-varying desired trajectory after suitable numbers of learning iterations. The main challenge for the iterative learning control design is that all the system parameters are iteration-varying. After separating the system parameters into a pure time-varying component and an iteration-varying component, the system dynamics are divided into an iteration-independent nominal part and an iteration-dependent uncertain part. An adaptive iterative learning controller is then designed to control the nominal dynamics and an iteration-varying boundary layer with dead-zone like auxiliary error is proposed to compensate for the iteration-varying uncertainties. The control parameters and the width of boundary layer are updated from trial to trial in order to guarantee the stability and convergence of the learning system. In addition to ensure the boundedness of control signals for each iteration and each time instant, we also prove that the norm of output error will asymptotically converge to a residual set whose size depends on the width of boundary layer as iteration number goes to infinity.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133730973","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}
Data fusion for parameter estimation with multi-structure and unequal-precision is considered in this paper. Matrix tools e.g., congruent transformation, trace function and matrix differential, are used to analyze the estimation performance. Theoretical results reveal that: the single equipment estimate, the optimal fusion estimate, and the joint estimate are some special cases of the fusion estimate. Moreover, the precisions of different fusion estimation methods are compared, the relations among which are provided in the following theorems. The performance of the four estimates are validated by the simulation of trajectory calculation for the V-2 missile.
{"title":"Data fusion for multi-structure and unequal-precision estimation","authors":"Zhangming He, Zhengfang Ma, Jiongqi Wang, Xuanying Zhou, Zhiwen Chen, Dayi Wang, Bowen Hou","doi":"10.1109/DDCLS.2017.8068068","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068068","url":null,"abstract":"Data fusion for parameter estimation with multi-structure and unequal-precision is considered in this paper. Matrix tools e.g., congruent transformation, trace function and matrix differential, are used to analyze the estimation performance. Theoretical results reveal that: the single equipment estimate, the optimal fusion estimate, and the joint estimate are some special cases of the fusion estimate. Moreover, the precisions of different fusion estimation methods are compared, the relations among which are provided in the following theorems. The performance of the four estimates are validated by the simulation of trajectory calculation for the V-2 missile.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133864474","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068144
Mingxuan Sun, Wen-ya Zhou, Qiang Chen
This paper addresses the problem of repetitive control for systems in which the reference input is a periodic signal, with known a period. The sliding mode repetitive controller based on switching-function dynamics is designed to eliminate the disturbances with the same period. In exponential reaching law, the sign function is replaced with power item to decrease chattering and a measure of disturbance-rejection is embedded in so as to solve the system uncertainties. In order to characterize the performance of closed system, monotone decreasing area boundary and absolute convergence layer boundary of switching-variate are defined and the bounds are derived in details.
{"title":"Switching-function dynamics based design of sliding mode repetitive controllers","authors":"Mingxuan Sun, Wen-ya Zhou, Qiang Chen","doi":"10.1109/DDCLS.2017.8068144","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068144","url":null,"abstract":"This paper addresses the problem of repetitive control for systems in which the reference input is a periodic signal, with known a period. The sliding mode repetitive controller based on switching-function dynamics is designed to eliminate the disturbances with the same period. In exponential reaching law, the sign function is replaced with power item to decrease chattering and a measure of disturbance-rejection is embedded in so as to solve the system uncertainties. In order to characterize the performance of closed system, monotone decreasing area boundary and absolute convergence layer boundary of switching-variate are defined and the bounds are derived in details.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122125602","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068106
T. Xiao, Xiao-dong Li
In many practical applications, the states, inputs and outputs of the systems show 2-dimensional (2-D) property and operate in a repetitive mode. A PID-type iterative learning control (ILC) is designed in this paper for 2-D system which can be described as a Roesser model and operates in a repetitive mode. The convergence conditions of the control algorithm are derived. In order to demonstrate the effectiveness of the proposed control method, simulations on a numeric example are performed.
{"title":"PID-type iterative learning control for 2-D Roesser model","authors":"T. Xiao, Xiao-dong Li","doi":"10.1109/DDCLS.2017.8068106","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068106","url":null,"abstract":"In many practical applications, the states, inputs and outputs of the systems show 2-dimensional (2-D) property and operate in a repetitive mode. A PID-type iterative learning control (ILC) is designed in this paper for 2-D system which can be described as a Roesser model and operates in a repetitive mode. The convergence conditions of the control algorithm are derived. In order to demonstrate the effectiveness of the proposed control method, simulations on a numeric example are performed.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"3 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123754667","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068081
Lei Zhang, Chunyang Fu, Xiaojun Guo, Yue Bai, Yantao Tian
In order to keep the attitude stability of the rotorcraft under external wind disturbance, the mathematical model of the rotorcraft under wind disturbance is established. Integral terms are added into the conventional backstepping method to resist the continuing wind disturbance which has a low amplitude. Adaptive control approach is used to compensate for the model error of rotorcraft and the sudden gust. The disadvantage of the conventional backstepping method in resisting the wind disturbance is improved. The simulation results show that the controllers designed by this method have better disturbance rejection performance and tracking performance.
{"title":"Adaptive backstepping control algorithm for a rotorcraft disturbed by different wind","authors":"Lei Zhang, Chunyang Fu, Xiaojun Guo, Yue Bai, Yantao Tian","doi":"10.1109/DDCLS.2017.8068081","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068081","url":null,"abstract":"In order to keep the attitude stability of the rotorcraft under external wind disturbance, the mathematical model of the rotorcraft under wind disturbance is established. Integral terms are added into the conventional backstepping method to resist the continuing wind disturbance which has a low amplitude. Adaptive control approach is used to compensate for the model error of rotorcraft and the sudden gust. The disadvantage of the conventional backstepping method in resisting the wind disturbance is improved. The simulation results show that the controllers designed by this method have better disturbance rejection performance and tracking performance.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"734 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123858169","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068150
Ding Jing, Ling Zhao, Darong Huang
A fault feature extraction model based on the PCA and wavelet packet is proposed to describe the characteristics of gearbox fault feature, which is expressed by low amplitude of the vibration signals, and easy to be disturbed by system and noise. Firstly, the PCA is used to reduce the correlation between the data dimension and the data. Then, the gearbox signals are decomposed by wavelet packet, and reconstructed based on the frequency bandwidth characteristics. After choosing those main frequency band which reflects the change of signal caused by the fault, and normalizing the selected frequency band, then the fault characteristic value is obtained. Finally, the vibration signal of the gearbox is treated as an example to verify the effectiveness of the method. The comparative analysis shows that the combination of PCA and wavelet packet is more effective than the wavelet packet.
{"title":"Incipient fault feature extraction method of gearbox based on wavelet package and PCA","authors":"Ding Jing, Ling Zhao, Darong Huang","doi":"10.1109/DDCLS.2017.8068150","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068150","url":null,"abstract":"A fault feature extraction model based on the PCA and wavelet packet is proposed to describe the characteristics of gearbox fault feature, which is expressed by low amplitude of the vibration signals, and easy to be disturbed by system and noise. Firstly, the PCA is used to reduce the correlation between the data dimension and the data. Then, the gearbox signals are decomposed by wavelet packet, and reconstructed based on the frequency bandwidth characteristics. After choosing those main frequency band which reflects the change of signal caused by the fault, and normalizing the selected frequency band, then the fault characteristic value is obtained. Finally, the vibration signal of the gearbox is treated as an example to verify the effectiveness of the method. The comparative analysis shows that the combination of PCA and wavelet packet is more effective than the wavelet packet.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128903266","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068076
Peng Huang, Haojie Lv
In view of the high cost and large volume of the traditional attitude heading reference system (ARHS) applied in the fields of spacecraft, robot and vehicle, a new method based on low cost MEMS sensor for AHRS is proposed. In this algorithm, the quaternion and three gyro drift are chosen as state vector to establish the state equation. At the same time, the components of acceleration of gravity and magnetic field intensity in the body fixed reference are considered as the observation vectors to establish the observation equation. In this algorithm, the earth's magnetic field is compensated by a special method. In order to evaluate the proposed quaternion-based extended Kalman filter algorithm, a micro quadrotor is developed. The results show that the proposed method can obtain the accurate attitude information and can suppress the white noise.
{"title":"The design of attitude heading reference system based on MEMS sensing technology","authors":"Peng Huang, Haojie Lv","doi":"10.1109/DDCLS.2017.8068076","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068076","url":null,"abstract":"In view of the high cost and large volume of the traditional attitude heading reference system (ARHS) applied in the fields of spacecraft, robot and vehicle, a new method based on low cost MEMS sensor for AHRS is proposed. In this algorithm, the quaternion and three gyro drift are chosen as state vector to establish the state equation. At the same time, the components of acceleration of gravity and magnetic field intensity in the body fixed reference are considered as the observation vectors to establish the observation equation. In this algorithm, the earth's magnetic field is compensated by a special method. In order to evaluate the proposed quaternion-based extended Kalman filter algorithm, a micro quadrotor is developed. The results show that the proposed method can obtain the accurate attitude information and can suppress the white noise.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461523","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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