In complex environments, many distributed multiagent systems are described with the fractional-order dynamics.In this paper, containment control of fractional-order multiagent systems with multiple leader agents are s...In complex environments, many distributed multiagent systems are described with the fractional-order dynamics.In this paper, containment control of fractional-order multiagent systems with multiple leader agents are studied. Firstly,the collaborative control of fractional-order multi-agent systems(FOMAS) with multiple leaders is analyzed in a directed network without delays. Then, by using Laplace transform and frequency domain theorem, containment consensus of networked FOMAS with time delays is investigated in an undirected network, and a critical value of delays is obtained to ensure the containment consensus of FOMAS. Finally, numerical simulations are shown to verify the results.展开更多
Formation control of discrete-time linear multi-agent systems using directed switching topology is considered in this work via a reduced-order observer, in which a formation control protocol is proposed under the assu...Formation control of discrete-time linear multi-agent systems using directed switching topology is considered in this work via a reduced-order observer, in which a formation control protocol is proposed under the assumption that each directed communication topology has a directed spanning tree. By utilizing the relative outputs of neighboring agents, a reduced-order observer is designed for each following agent. A multi-step control algorithm is established based on the Lyapunov method and the modified discrete-time algebraic Riccati equation. A sufficient condition is given to ensure that the discrete-time linear multi-agent system can achieve the expected leader-following formation.Finally, numerical examples are provided so as to demonstrate the effectiveness of the obtained results.展开更多
Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also ...Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time.Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective influence.Although many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement.This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is recommended.First,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well loggings.Then,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning model.The performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are compared.The other two wells are used for model validation.The research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respectively.The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models.This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.展开更多
Dissipative solitons have been realized in mode-locked fiber lasers in the theoretical framework of the Ginzburg±Landau equation and have significantly improved the pulse energy and peak power levels of such lase...Dissipative solitons have been realized in mode-locked fiber lasers in the theoretical framework of the Ginzburg±Landau equation and have significantly improved the pulse energy and peak power levels of such lasers.It is interesting to explore whether dissipative solitons exist in optical parametric oscillators in the framework of three-wave coupling equations in order to substantially increase the performance of optical parametric oscillators.Here,we demonstrate a temporalfiltering dissipative soliton in a synchronously pumped optical parametric oscillator.The temporal-gain filtering of the pump pulse combined with strong cascading nonlinearity and dispersion in the optical parametric oscillator enables the generation of a broad spectrum with a nearly linear chirp;consequently,a significantly compressed pulse and high peak power can be realized after dechirping outside the cavity.Furthermore,we realized,for the first time,dissipative solitons in an optical system with a negative nonlinear phase shift and anomalous dispersion,extending the parameter region of dissipative solitons.The findings may open a new research block for dissipative solitons and provide new opportunities for mid-infrared ultrafast science.展开更多
This paper studies the sampled data based containment control problem of second-order multi-agent systems with intermittent communications,where velocity measurements for each agent are unavailable.A novel controller ...This paper studies the sampled data based containment control problem of second-order multi-agent systems with intermittent communications,where velocity measurements for each agent are unavailable.A novel controller for second-order containment is put forward via intermittent sampled position data measurement.Several necessary and sufficient conditions are derived to achieve intermittent sampled containment control by means of analyzing the relationship among control gains,eigenvalues of the Laplacian matrix,the sampling period,and the communication width.Finally,several simulation examples are used to testify the correctness and effectiveness of the theoretical results.展开更多
基金supported by the National Natural Science Foundation of China(61273200,61273152,61202111,61304052,51407088)the Science Foundation of Education Office of Shandong Province of China(ZR2011FM07,BS2015DX018)
文摘In complex environments, many distributed multiagent systems are described with the fractional-order dynamics.In this paper, containment control of fractional-order multiagent systems with multiple leader agents are studied. Firstly,the collaborative control of fractional-order multi-agent systems(FOMAS) with multiple leaders is analyzed in a directed network without delays. Then, by using Laplace transform and frequency domain theorem, containment consensus of networked FOMAS with time delays is investigated in an undirected network, and a critical value of delays is obtained to ensure the containment consensus of FOMAS. Finally, numerical simulations are shown to verify the results.
基金supported by National Natural Science Foundation of China(61573200,61973175)the Fundamental Research Funds for the Central Universities,Nankai University(63201196)。
文摘Formation control of discrete-time linear multi-agent systems using directed switching topology is considered in this work via a reduced-order observer, in which a formation control protocol is proposed under the assumption that each directed communication topology has a directed spanning tree. By utilizing the relative outputs of neighboring agents, a reduced-order observer is designed for each following agent. A multi-step control algorithm is established based on the Lyapunov method and the modified discrete-time algebraic Riccati equation. A sufficient condition is given to ensure that the discrete-time linear multi-agent system can achieve the expected leader-following formation.Finally, numerical examples are provided so as to demonstrate the effectiveness of the obtained results.
文摘Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time.Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective influence.Although many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement.This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is recommended.First,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well loggings.Then,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning model.The performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are compared.The other two wells are used for model validation.The research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respectively.The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models.This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.
基金the National Natural Science Foundation of China(Nos.61675130,62075126 and 91850203)。
文摘Dissipative solitons have been realized in mode-locked fiber lasers in the theoretical framework of the Ginzburg±Landau equation and have significantly improved the pulse energy and peak power levels of such lasers.It is interesting to explore whether dissipative solitons exist in optical parametric oscillators in the framework of three-wave coupling equations in order to substantially increase the performance of optical parametric oscillators.Here,we demonstrate a temporalfiltering dissipative soliton in a synchronously pumped optical parametric oscillator.The temporal-gain filtering of the pump pulse combined with strong cascading nonlinearity and dispersion in the optical parametric oscillator enables the generation of a broad spectrum with a nearly linear chirp;consequently,a significantly compressed pulse and high peak power can be realized after dechirping outside the cavity.Furthermore,we realized,for the first time,dissipative solitons in an optical system with a negative nonlinear phase shift and anomalous dispersion,extending the parameter region of dissipative solitons.The findings may open a new research block for dissipative solitons and provide new opportunities for mid-infrared ultrafast science.
基金Project supported by the Tianjin Natural Science Foundation of China(Nos.20JCQNJC01450 and 20JCYBJC01060)the National Natural Science Foundation of China(No.61973175)。
文摘This paper studies the sampled data based containment control problem of second-order multi-agent systems with intermittent communications,where velocity measurements for each agent are unavailable.A novel controller for second-order containment is put forward via intermittent sampled position data measurement.Several necessary and sufficient conditions are derived to achieve intermittent sampled containment control by means of analyzing the relationship among control gains,eigenvalues of the Laplacian matrix,the sampling period,and the communication width.Finally,several simulation examples are used to testify the correctness and effectiveness of the theoretical results.
