Title :
On convergence properties of a sensitivity penalization based robust state estimator
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
Asymptotic properties are re-investigated in this paper for the robust state estimator derived in [14]. A new formula is derived for the update of the pseudo-covariance matrix of estimation errors. Based on this formula, the restrictive orthogonality condition of [14] is successfully removed. Under the situation that plant nominal parameters are time-invariant, it is shown that, when some stabilizability and detectability conditions are satisfied, the robust estimator converges to a stable time invariant system. Moreover, when the system is exponentially stable, it has been proved that this estimate is asymptotically unbiased and its estimation errors are upper bounded.
Keywords :
asymptotic stability; convergence; covariance matrices; sensitivity analysis; state estimation; asymptotic property; convergence property; detectability condition; estimation errors; exponential stability; plant nominal parameter; pseudo-covariance matrix; restrictive orthogonality condition; robust state estimator; sensitivity penalization; time invariant system stability; Convergence; Covariance matrix; Equations; Estimation error; Mathematical model; Robustness; recursive estimation; robustness; sensitivity penalization; state estimation; structured parametric uncertainty;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717631