Abstract
The problem of adaptive estimation of constant parameters in the linear regressor model is studied without the hypothesis that regressor is persistently excited. First, the initial vector estimation problem is transformed to a series of the scalar ones using the method of dynamic regressor extension and mixing. Second, several adaptive estimation algorithms are proposed for the scalar scenario. In such a case, if the regressor is nullified asymptotically or in finite time, then the problem of estimation is also posed on a finite interval of time. Robustness of the proposed algorithms with respect to measurement noise and exogenous disturbances is analyzed. The efficiency of the designed estimators is demonstrated in numeric experiments for academic examples.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
