Pub Date : 2019-05-01DOI: 10.1109/DDCLS.2019.8909081
Xiaoyang Meng, Yajiang Du, Zonggang Li, Yinjuan Chen
This note considers the formation control of heterogeneous multi-agent systems with time delay, in which all agents are divided into a leader and followers. Here, the output regulation method is employed such that followers track the leader and finally converge to a desired formation as there exists communication time-delay. For this purpose, a distributed observer is proposed for each follower to estimate the state of the leader, and then employ a feedback controller to update its states. As the distances among followers and leader are predefined, we have shown that the considered heterogeneous multi-agent systems with time-delay can achieve the desired formation. Simulation example is included to illustrate the validity of the proposed method.
{"title":"Formation Control of Heterogeneous Multi-Agent Systems with Time-Delay Based on Output Regulation","authors":"Xiaoyang Meng, Yajiang Du, Zonggang Li, Yinjuan Chen","doi":"10.1109/DDCLS.2019.8909081","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909081","url":null,"abstract":"This note considers the formation control of heterogeneous multi-agent systems with time delay, in which all agents are divided into a leader and followers. Here, the output regulation method is employed such that followers track the leader and finally converge to a desired formation as there exists communication time-delay. For this purpose, a distributed observer is proposed for each follower to estimate the state of the leader, and then employ a feedback controller to update its states. As the distances among followers and leader are predefined, we have shown that the considered heterogeneous multi-agent systems with time-delay can achieve the desired formation. Simulation example is included to illustrate the validity of the proposed method.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"90 1","pages":"249-254"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79401245","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908867
Wei Wang, Hao Zhang
The stator and armature coils of the transverse flux permanent magnet motor are perpendicular to each other in space. Its core size and coil size are independent of each other, size can be arbitrarily selected in a certain range. Thus the high torque density is suitable for some special occasions. First of all, the finite element build accurate stator system model. Secondly we calculated modal of the motor, according to the results of calculation analysis concluded that the stator system analytic calculation of the equivalent model. The above work has been the calculation formula of the stator system natural frequency that provide calculation basis for transverse flux motor vibration noise analysis.
{"title":"The Analysis of Stator System Natural Frequency Calculation Method on Transverse Flux Permanent Magnet Motor","authors":"Wei Wang, Hao Zhang","doi":"10.1109/DDCLS.2019.8908867","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908867","url":null,"abstract":"The stator and armature coils of the transverse flux permanent magnet motor are perpendicular to each other in space. Its core size and coil size are independent of each other, size can be arbitrarily selected in a certain range. Thus the high torque density is suitable for some special occasions. First of all, the finite element build accurate stator system model. Secondly we calculated modal of the motor, according to the results of calculation analysis concluded that the stator system analytic calculation of the equivalent model. The above work has been the calculation formula of the stator system natural frequency that provide calculation basis for transverse flux motor vibration noise analysis.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"23 1","pages":"1087-1090"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78806983","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 : 2019-05-01DOI: 10.1109/ddcls.2019.8908968
Fei Wang, Zao Feng, Guoyong Huang, Xuefeng Zhu, Yang Li
Aiming at the detection problem of Multiple blockage in urban water supply pipelines and drainage pipelines, also the problem of distinguishing commonly used pipe components such as lateral connection from the actual blocking conditions. A multiple-blocking fault identification method based on support vector machine (SVM) combined with a feature extraction approach for component signal are proposed in this paper. Firstly, the variational mode decomposition (VMD) was applied on the acoustic signals collected in the pipeline to obtain a set of finite bandwidth natural mode functions (IMF), multiple time domain indices and center frequencies were extracted as features, then a feature vector set can be constructed and input into the SVM classifier. The experimental results have shown that the method based on VMD feature fusion and support vector machine can effectively identify the multiple congestion faults of drainage pipelines. In addition, the method was compared with back propagation (BP)neural network and the k-nearest neighbor algorithm (KNN). The results suggest that the proposed method has a better performance on the partial blockage recognition with a small number of training samples.
{"title":"Multiple Blockage Identification of Drainage Pipeline Based on VMD Feature Fusion and Support Vector Machine","authors":"Fei Wang, Zao Feng, Guoyong Huang, Xuefeng Zhu, Yang Li","doi":"10.1109/ddcls.2019.8908968","DOIUrl":"https://doi.org/10.1109/ddcls.2019.8908968","url":null,"abstract":"Aiming at the detection problem of Multiple blockage in urban water supply pipelines and drainage pipelines, also the problem of distinguishing commonly used pipe components such as lateral connection from the actual blocking conditions. A multiple-blocking fault identification method based on support vector machine (SVM) combined with a feature extraction approach for component signal are proposed in this paper. Firstly, the variational mode decomposition (VMD) was applied on the acoustic signals collected in the pipeline to obtain a set of finite bandwidth natural mode functions (IMF), multiple time domain indices and center frequencies were extracted as features, then a feature vector set can be constructed and input into the SVM classifier. The experimental results have shown that the method based on VMD feature fusion and support vector machine can effectively identify the multiple congestion faults of drainage pipelines. In addition, the method was compared with back propagation (BP)neural network and the k-nearest neighbor algorithm (KNN). The results suggest that the proposed method has a better performance on the partial blockage recognition with a small number of training samples.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"107 1","pages":"820-825"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76097801","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 : 2019-05-01DOI: 10.1109/ddcls.2019.8909082
Jihui Luo, Guoyong Huang, Jun Ma
This paper proposes a check valve fault diagnosis method based on time-frequency images and Non-negative Matrix Factorization (NMF), which transforms the fault features extraction of time domain signals into fault features extraction of time-frequency images. Firstly, the vibration signals of the check valve are decomposed by Differential Empirical Mode Decomposition (DEMD), and the Intrinsic Mode Functions (IMFs) containing more feature information are selected to reconstruct the signals by correlation coefficient method. Secondly, Wigner-Ville Distribution (WVD) is used to analysis the reconstruct signals and obtain the time-frequency images, then NMF is applied to decompose the time-frequency image matrixes and get the feature matrix. Finally, the feature vectors are classified via the Support Vector Machine (SVM) which is optimized by Genetic Algorithm (GA) to complete the fault diagnosis of the high pressure diaphragm pump check valve. The method is validated using data from three operating states of the high pressure diaphragm pump check valve. The experimental result shows that the proposed method can effectively extract the fault features and identify fault types of the check valve. The average classification accuracy rate is up to 99.17%, which is higher than using the time domain and frequency domain features as input.
{"title":"Fault Diagnosis of Check Valve Based on WVD and NMF","authors":"Jihui Luo, Guoyong Huang, Jun Ma","doi":"10.1109/ddcls.2019.8909082","DOIUrl":"https://doi.org/10.1109/ddcls.2019.8909082","url":null,"abstract":"This paper proposes a check valve fault diagnosis method based on time-frequency images and Non-negative Matrix Factorization (NMF), which transforms the fault features extraction of time domain signals into fault features extraction of time-frequency images. Firstly, the vibration signals of the check valve are decomposed by Differential Empirical Mode Decomposition (DEMD), and the Intrinsic Mode Functions (IMFs) containing more feature information are selected to reconstruct the signals by correlation coefficient method. Secondly, Wigner-Ville Distribution (WVD) is used to analysis the reconstruct signals and obtain the time-frequency images, then NMF is applied to decompose the time-frequency image matrixes and get the feature matrix. Finally, the feature vectors are classified via the Support Vector Machine (SVM) which is optimized by Genetic Algorithm (GA) to complete the fault diagnosis of the high pressure diaphragm pump check valve. The method is validated using data from three operating states of the high pressure diaphragm pump check valve. The experimental result shows that the proposed method can effectively extract the fault features and identify fault types of the check valve. The average classification accuracy rate is up to 99.17%, which is higher than using the time domain and frequency domain features as input.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"205 1","pages":"782-786"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77475542","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909031
Yunfei Mu, Zilong Tan, Huaguang Zhang, Juan Zhang
This paper focuses on proportional derivative observer design for a class of Takagi-Sugeno fuzzy singular systems. According to the available knowledge on premise variables, first, observer design with known premise variables is considered, and explicit parametrization of the desired observer is also given. Moreover, observer design with unknown premise variables is further investigated. Some new conditions, which guarantee the error system to be robust stability, are derived. All the stability criterions are presented in linear matrix inequalities framework, which can be conveniently verified via Matlab. Finally, two illustrative examples are simulated to illustrate the correctness of the present schemes.
{"title":"Proportional Derivative Observer Design for Nonlinear Singular Systems","authors":"Yunfei Mu, Zilong Tan, Huaguang Zhang, Juan Zhang","doi":"10.1109/DDCLS.2019.8909031","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909031","url":null,"abstract":"This paper focuses on proportional derivative observer design for a class of Takagi-Sugeno fuzzy singular systems. According to the available knowledge on premise variables, first, observer design with known premise variables is considered, and explicit parametrization of the desired observer is also given. Moreover, observer design with unknown premise variables is further investigated. Some new conditions, which guarantee the error system to be robust stability, are derived. All the stability criterions are presented in linear matrix inequalities framework, which can be conveniently verified via Matlab. Finally, two illustrative examples are simulated to illustrate the correctness of the present schemes.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"46 1","pages":"111-116"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77555053","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909010
Rui Li, Rongmin Cao, Yingnian Wu, Di Yu
In order to solve the complex and difficult identification problems of overhead transmission line fault diagnosis, and to improve the accuracy of classification effectively, a new method of fault diagnosis for overhead transmission line is proposed in this paper. Firstly, the collected traveling wave signals are processed by HHT (Hilbert-Huang Transform) to realize joint feature extraction in time-frequency domain. And a data-driven lightning strike warning model for transmission lines is adopted. The model includes PCA (principal component analysis), data acquisition and preprocessing, data analysis and prediction, and model online correction. For eliminating the influence of noise and singularity on fault diagnosis; then input training set and production rules to train the intelligent classification method, by which exact fault diagnosis model was obtained. Finally, apply the algorithm to the intelligent lightning traveling wave monitoring system of an actual 500 kV transmission line, the experimental results show that the proposed method can not only calculate the exact location of fault points, but also accurately classified them that classified both single fault and multi-fault, which opens up a new approach for overhead transmission line to intelligent fault diagnosis.
{"title":"Location and Recognition System for Lightning Fault of Transmission Line Based on Data-driven Technology","authors":"Rui Li, Rongmin Cao, Yingnian Wu, Di Yu","doi":"10.1109/DDCLS.2019.8909010","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909010","url":null,"abstract":"In order to solve the complex and difficult identification problems of overhead transmission line fault diagnosis, and to improve the accuracy of classification effectively, a new method of fault diagnosis for overhead transmission line is proposed in this paper. Firstly, the collected traveling wave signals are processed by HHT (Hilbert-Huang Transform) to realize joint feature extraction in time-frequency domain. And a data-driven lightning strike warning model for transmission lines is adopted. The model includes PCA (principal component analysis), data acquisition and preprocessing, data analysis and prediction, and model online correction. For eliminating the influence of noise and singularity on fault diagnosis; then input training set and production rules to train the intelligent classification method, by which exact fault diagnosis model was obtained. Finally, apply the algorithm to the intelligent lightning traveling wave monitoring system of an actual 500 kV transmission line, the experimental results show that the proposed method can not only calculate the exact location of fault points, but also accurately classified them that classified both single fault and multi-fault, which opens up a new approach for overhead transmission line to intelligent fault diagnosis.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"55 1","pages":"952-956"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74182270","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908905
X. Dai, Fan Zhang, Tingting Zhao
This paper studies the problem of iterative learning control for a linear fractional-order distributed parameter systems with variable tracking trajectory. An improved P-type updating control law is employed to estimate the spatial-temporal varying curve surface iteratively. Then, the sufficient conditions of convergence for output error of the system in the sense of $L_{2}$ norm has been revised through rigorous analysis. The numerical results show the effectiveness of the proposed ILC scheme.
{"title":"Iterative Learning Control for Linear Fractional-Order Distributed Parameter Systems with Variable Tracking Trajectory","authors":"X. Dai, Fan Zhang, Tingting Zhao","doi":"10.1109/DDCLS.2019.8908905","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908905","url":null,"abstract":"This paper studies the problem of iterative learning control for a linear fractional-order distributed parameter systems with variable tracking trajectory. An improved P-type updating control law is employed to estimate the spatial-temporal varying curve surface iteratively. Then, the sufficient conditions of convergence for output error of the system in the sense of $L_{2}$ norm has been revised through rigorous analysis. The numerical results show the effectiveness of the proposed ILC scheme.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"19 1","pages":"360-365"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74250589","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909054
Shoulin Hao, Tao Liu
This paper proposes a high-order internal model (HOIM) based indirect-type iterative learning control (ILC) scheme for batch processes subject to batch-varying initial condition and reference along with external disturbance. A widely used proportional-integral (PI) control structure in practical applications is taken as the inner loop, while the set-point related indirect-type ILC updating law is designed independent of the inner loop to robustly track the desired output trajectory. In comparison with the existing indirect-type ILC methods, the set-point commands and output tracking errors over more than one previous batches are used for the ILC design in terms of an augmented HOIM associated with the initial process state, reference, and external disturbance. By using an equivalent 2D Roesser system description of the closed-loop ILC system, a sufficient condition in terms of linear matrix inequality is established to ensure asymptotic stability of the resulting 2D system together with a 2D $mathcal{H}_{infty}$ performance under non-zero boundary conditions. Finally, the obtained results are validated by an illustrative example of injection molding.
{"title":"High-Order Internal Model Based Indirect-Type Iterative Learning Control Design for Batch Processes with Batch-Varying Factors","authors":"Shoulin Hao, Tao Liu","doi":"10.1109/DDCLS.2019.8909054","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909054","url":null,"abstract":"This paper proposes a high-order internal model (HOIM) based indirect-type iterative learning control (ILC) scheme for batch processes subject to batch-varying initial condition and reference along with external disturbance. A widely used proportional-integral (PI) control structure in practical applications is taken as the inner loop, while the set-point related indirect-type ILC updating law is designed independent of the inner loop to robustly track the desired output trajectory. In comparison with the existing indirect-type ILC methods, the set-point commands and output tracking errors over more than one previous batches are used for the ILC design in terms of an augmented HOIM associated with the initial process state, reference, and external disturbance. By using an equivalent 2D Roesser system description of the closed-loop ILC system, a sufficient condition in terms of linear matrix inequality is established to ensure asymptotic stability of the resulting 2D system together with a 2D $mathcal{H}_{infty}$ performance under non-zero boundary conditions. Finally, the obtained results are validated by an illustrative example of injection molding.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"52 1","pages":"400-405"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81115754","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908878
Yu-ping Qin, Yuanyue Zhao, Xiangna Li, Q. Leng
A multi-class classification algorithm based on hypercube is proposed. For each class of training samples, a minimum hypercube that surround all samples is constructed in sample space. If two hypercubes intersect, the hypercube centers are used as the benchmark for compression. For a sample to be classified, its class label is determined according to the hypercube in which it is located. If this sample is not in any hypercube, the distances from the sample to the center of each hypercube are calculated firstly, and then the class label is determined by the nearest neighbor rule. The experimental results show that the training speed and classification speed of the proposed algorithm are improved significantly while ensuring the classification accuracy, especially in the case of large dataset and large number of classes.
{"title":"A Multi-class Classification Algorithm Based on Hypercube","authors":"Yu-ping Qin, Yuanyue Zhao, Xiangna Li, Q. Leng","doi":"10.1109/DDCLS.2019.8908878","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908878","url":null,"abstract":"A multi-class classification algorithm based on hypercube is proposed. For each class of training samples, a minimum hypercube that surround all samples is constructed in sample space. If two hypercubes intersect, the hypercube centers are used as the benchmark for compression. For a sample to be classified, its class label is determined according to the hypercube in which it is located. If this sample is not in any hypercube, the distances from the sample to the center of each hypercube are calculated firstly, and then the class label is determined by the nearest neighbor rule. The experimental results show that the training speed and classification speed of the proposed algorithm are improved significantly while ensuring the classification accuracy, especially in the case of large dataset and large number of classes.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"406-409"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84838117","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909027
Tongshu Wang, Xuxi Zhang
This paper investigates the problem of consensus tracking for linear multi-agent systems with exogenous disturbances, which the disturbances are generated by linear external exosystems. To solve consensus tracking problem, a distributed adaptive controller for each follower has been considered. For the exogenous disturbances caused by the external systems, a disturbance observer is designed to deal with exogenous disturbances. Supposing that the communication graph among followers is undirected. Then, by applying the Lyapunov function method, the consensus tracking problem with exogenous disturbances was proved. Finally, a simulation is given to illustrate the effectiveness of our results.
{"title":"Consensus Tracking of Linear Multi-Agent Systems With Exogenous Disturbances","authors":"Tongshu Wang, Xuxi Zhang","doi":"10.1109/DDCLS.2019.8909027","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909027","url":null,"abstract":"This paper investigates the problem of consensus tracking for linear multi-agent systems with exogenous disturbances, which the disturbances are generated by linear external exosystems. To solve consensus tracking problem, a distributed adaptive controller for each follower has been considered. For the exogenous disturbances caused by the external systems, a disturbance observer is designed to deal with exogenous disturbances. Supposing that the communication graph among followers is undirected. Then, by applying the Lyapunov function method, the consensus tracking problem with exogenous disturbances was proved. Finally, a simulation is given to illustrate the effectiveness of our results.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"35 1","pages":"282-286"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86917442","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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