An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying...An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying Bernoulli random binary distribution is introduced and a new system model is established by employing the measurements with random delay.By using the linear matrix inequality(LMI) technique,sufficient conditions are derived for ensuring the mean-square stochastic stability of the filtering error systems and guaranteeing a prescribed H∞ filtering performance.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approach.展开更多
The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm- bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is ...The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm- bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is obtained which involves unknown inputs represented by disturbances, model uncertainties and time-delays. As to the nominal system, sufficient conditions are provided for the existence of the mode-dependent H∞ filter by selecting the appropriate Lyapunov-Krasovskii function and the robust H∞ filter is proposed for the jump system while considering the time-delays and uncertainties. Both of above conditions for the existence of the H∞ filter and roust H∞ filter are presented in terms of linear matrix inequalities, and convex optimization problems are formulated to design the desired filters. By employing the proposed mode-dependent H∞ filter, the systems have the stochastic stability and better ability of restraining disturbances stochastically, and the given prescribed H∞ performance is guaranteed. Simulation resuhs illustrate the effectiveness of developed techniques.展开更多
This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained optimization.In order to deal with large scale problems,a reduced Hessian matrix is ...This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained optimization.In order to deal with large scale problems,a reduced Hessian matrix is approximated by BFGS updates.The new method assures global convergence without using a merit function.By Lagrangian function in the filter and nonmonotone scheme,the authors prove that the method can overcome Maratos effect without using second order correction step so that the locally superlinear convergence is achieved.The primary numerical experiments are reported to show effectiveness of the proposed algorithm.展开更多
The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted...The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted states to evaluate the covariance matrices of observations and dynamic model errors at the present epoch.The weight function is constructed based on the variances of observational residuals or predicted state residuals and the space distance between the previous and the present epoch.In order to balance the contributions of the measurements and the dynamic model information,an adaptive factor is applied by using a two-segment function and predicted state discrepancy statistics.Two applications,orbit determination of a maneuvered GEO satellite and GPS kinematic positioning,are conducted to verify the performance of the proposed method.展开更多
基金National Natural Science Foundations of China (No. 60474079,No. 60704024,No. 60774060,No. 61074025,and No. 61074024)
文摘An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying Bernoulli random binary distribution is introduced and a new system model is established by employing the measurements with random delay.By using the linear matrix inequality(LMI) technique,sufficient conditions are derived for ensuring the mean-square stochastic stability of the filtering error systems and guaranteeing a prescribed H∞ filtering performance.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approach.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60574001)Program for New Century Excellent Talents in University(Grant No.NCET-05-0485)
文摘The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm- bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is obtained which involves unknown inputs represented by disturbances, model uncertainties and time-delays. As to the nominal system, sufficient conditions are provided for the existence of the mode-dependent H∞ filter by selecting the appropriate Lyapunov-Krasovskii function and the robust H∞ filter is proposed for the jump system while considering the time-delays and uncertainties. Both of above conditions for the existence of the H∞ filter and roust H∞ filter are presented in terms of linear matrix inequalities, and convex optimization problems are formulated to design the desired filters. By employing the proposed mode-dependent H∞ filter, the systems have the stochastic stability and better ability of restraining disturbances stochastically, and the given prescribed H∞ performance is guaranteed. Simulation resuhs illustrate the effectiveness of developed techniques.
基金supported by the National Science Foundation of China under Grant No.10871130the Ph.D Foundation under Grant No.20093127110005+1 种基金the Shanghai Leading Academic Discipline Project under Grant No.S30405the Innovation Program of Shanghai Municipal Education Commission under Grant No.12YZ174
文摘This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained optimization.In order to deal with large scale problems,a reduced Hessian matrix is approximated by BFGS updates.The new method assures global convergence without using a merit function.By Lagrangian function in the filter and nonmonotone scheme,the authors prove that the method can overcome Maratos effect without using second order correction step so that the locally superlinear convergence is achieved.The primary numerical experiments are reported to show effectiveness of the proposed algorithm.
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China (Grant No.2007B51)the National Natural Science Foundation of China (Grant Nos.41174008 and 41020144004)+1 种基金China Postdoctoral Science Foundation (Grant Nos.20080430148 and 200902444)the Junior Scientist Exchange Program between the China Scholarship Council and the Helmholtz Association of German Research Centers Council
文摘The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted states to evaluate the covariance matrices of observations and dynamic model errors at the present epoch.The weight function is constructed based on the variances of observational residuals or predicted state residuals and the space distance between the previous and the present epoch.In order to balance the contributions of the measurements and the dynamic model information,an adaptive factor is applied by using a two-segment function and predicted state discrepancy statistics.Two applications,orbit determination of a maneuvered GEO satellite and GPS kinematic positioning,are conducted to verify the performance of the proposed method.
