Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri...Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.展开更多
The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to time-delay.First,using the dynamic change of coordinates,the problem of global state feedback stabilization is...The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to time-delay.First,using the dynamic change of coordinates,the problem of global state feedback stabilization is solved for a class of time-delay systems under a type of nonhomogeneous growth conditions.With the aid of an appropriate Lyapunov-Krasovskii functional and the adaptive strategy used in coordinates,the closed-loop system can be globally asymptotically stabilized by the dynamic linear state feedback controller.The growth condition in perturbations are more general than that in the existing results.The correctness of the theoretical results are illustrated with an academic simulation example.展开更多
基金This work is supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0467)Jiangsu Province,China.During the revision of this paper,the author is supported by China Scholarship Council(No.201906840021)China to continue some research related to data processing.
文摘Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
基金supported by US National Science Foundation under Grant No.HRD-0932339the National Natural Science Foundation of China under Grant Nos.61374038,61374050,61273119,61174076+1 种基金the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2011253Research Fund for the Doctoral Program of Higher Education of China under Grant No.20110092110021
文摘The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to time-delay.First,using the dynamic change of coordinates,the problem of global state feedback stabilization is solved for a class of time-delay systems under a type of nonhomogeneous growth conditions.With the aid of an appropriate Lyapunov-Krasovskii functional and the adaptive strategy used in coordinates,the closed-loop system can be globally asymptotically stabilized by the dynamic linear state feedback controller.The growth condition in perturbations are more general than that in the existing results.The correctness of the theoretical results are illustrated with an academic simulation example.
