Title :
The Kalman filter based recursive algorithm: windup and its avoidance
Author :
Cao, Liyu ; Schwartz, Howard M.
Author_Institution :
Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, Ont., Canada
Abstract :
A theoretical analysis is given which shows that the covariance matrix in the Kalman filter based parameter estimator increases at least linearly when the input is not persistently exciting. This windup phenomenon which is the same as that in the exponential forgetting least squares algorithm, is undesirable and even unacceptable in some applications. To overcome it a new algorithm is proposed, in which the constant covariance matrix of the parameter variation is replaced by a time-varying sequence consisting of the regression vector
Keywords :
Kalman filters; covariance matrices; filtering theory; least squares approximations; parameter estimation; Kalman filter; covariance matrix; least squares; parameter estimation; recursive algorithm; windup; Algorithm design and analysis; Covariance matrix; Drives; Eigenvalues and eigenfunctions; Least squares approximation; Least squares methods; Parameter estimation; Recursive estimation; Systems engineering and theory; Windup;
Conference_Titel :
American Control Conference, 2001. Proceedings of the 2001
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-6495-3
DOI :
10.1109/ACC.2001.946194
