Design And Study Of Estimator For Discrete System With Unknown Input And Delay

The estimation problems for systems with unknown inputs have been widely applied in the fields of average precipitation in a storm,demographic studies,physiological systems,communication engineering and so on.The estimation for systems with unknown inputs is very complicated due to unobservability of unknown input and uncertainty of system.Firstly,unbiased minimum variance estimation and the reorganized innovation method is used to solve the problem of simultaneous input and state estimation for linear systems with unknown inputs without any prior information in this paper,In addition,the reorganized innovation method is used to deal with the time-delay problem for the observation time-delay system with unknown input.Finally,The stability of the time-invariant estimator is analyzed in infinite horizon by analyzing the convergence of Riccati equation.The main research work of this paper includes the following points:(1)For measurement-delay systems with unknown inputs,the optimal estimation algorithm is studied in the sense of unbiased minimum variance,Then we utilize the innovation to obtain the unknown input estimator by least-squares estimation and the optimal state estimator is constructed by transforming into a standard Kalman filtering,the design strategy of unbiased estimator is generalized.(2)The measurement-delay system is converted into a delay free system by using the reorganized innovation approach.the estimator is derived in terms of two Riccati difference equations of the same dimension with the state model,to solve the problem that the dimension of the extended dimension system is higher and the computation is too large or even unsolvable due to the large time delay.To design the optimal estimator.(3)We develop a parallel and prove that the error covariance of state estimation is close to the unique fixed point to obtain the stability properties of theproposed time-invariant unbiased filter estimator under standard conditions by introducing the appropriate operator flexibly.(4)Aiming at the designed estimator,we give numerical examples and simulation experiment to estimate performance of estimator by utilizing Matlab to show that our estimation approach is effective.