.png){kind=icon}
.png){kind=icon}
Return
A generalized projection estimation algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2024.111942.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Given a linear regression model in discrete-time containing a vector of p constant uncertain parameters, this paper addresses the problem of designing an exponentially convergent parameter estimation algorithm, even when the regressor vector is not persistently exciting (not even in a finite time interval). On the basis of the definition of lack of persistency of excitation of order q for the regressor vector, 0 <= q <= p (which coincides with the classical definition of persistency of excitation when q = 0), a generalized projection estimation algorithm is proposed which guarantees global exponential convergence of the parameter estimation error and allows for the on-line computation of the order q of the lack of persistency of excitation. When the lack of persistency of excitation is of order zero, global exponential convergence to zero of the parameter estimation error is obtained, recovering a well-known result and the projection estimation algorithm as a special case.
Keywords:
Exponentially convergent estimation
Projection estimation algorithm
Discrete-time systems
Linear regression model
Parameter estimation
Persistency of excitation