In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy...In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation.Then,considering that the leader state is not available to each follower under DoS attacks,a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader.Finally,adaptive radial basis function neural network(RBFNN) is used to approximate the unknown ensemble disturbances in the system,and the fixed-time time-varying formation scheme is designed with the constructed observer.The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.展开更多
The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in ...The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.展开更多
Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an impr...Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.展开更多
文摘In this paper,the fixed-time time-varying formation of heterogeneous multi-agent systems(MASs) based on tracking error observer under denial-of-service(DoS) attacks is investigated.Firstly,the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation.Then,considering that the leader state is not available to each follower under DoS attacks,a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader.Finally,adaptive radial basis function neural network(RBFNN) is used to approximate the unknown ensemble disturbances in the system,and the fixed-time time-varying formation scheme is designed with the constructed observer.The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.
基金supported by the Fundamental Research Funds for the Central Universities(ZYGX2009J016)
文摘The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.
基金Ministry of Science and Technology of the People’s Republic of China for its support and guidance(Grant No.2018YFC0214100)。
文摘Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.
