Title :
A comparison of classical stochastic estimation and deterministic robust estimation
Author :
Krause, James M. ; Khargonekar, Pramod P.
Author_Institution :
Honeywell Syst. & Res. Center, Minneapolis, MN, USA
fDate :
7/1/1992 12:00:00 AM
Abstract :
The formulation and solution of two linear parameter estimation problems are compared. The basic distinction in the problem formulation is the nature of the uncertainty. In one case, the uncertainty is generated by white Gaussian noise, and the solution is the Kalman filter. In the other case, the uncertainty is unmodeled dynamics in the unit ball in H∞ or its nonlinear cover, and the particular solution studied is a deterministic robust estimator. Certain parallels between classical stochastic estimation (Kalman filtering) and the deterministic robust estimation are examined. The similarities and differences are discussed in geometric terms, in philosophical terms, and in terms of the estimator´s recursive implementation
Keywords :
Kalman filters; parameter estimation; Kalman filter; classical stochastic estimation; deterministic robust estimation; geometric terms; linear parameter estimation; philosophical terms; recursive implementation; uncertainty; unmodeled dynamics; white Gaussian noise; Control design; Control systems; Error correction; Frequency; Iterative methods; Parameter estimation; Robust control; Robustness; Stochastic processes; System identification;
Journal_Title :
Automatic Control, IEEE Transactions on