Abstract
In this paper, we propose a robust self-tuning controller (STC) under outliers. A parameter update law of a conventional STC consists of a recursive least squares estimation, and the estimation is given by a solution of a minimization problem of estimated errors. In the proposed method, we estimate parameters and outliers explicitly by addition of a l1 regression to the minimization problem like a robust Kalman filter via l1 regression, and the estimated outliers are removed from measurement outputs in the controller. We also analyze control performances of the proposed method under outliers, and it is shown theoretically that performances in the proposed method with outliers are nearly equal to ones in the normal STC without outliers. Numerical simulation, in which a controlled object is a non-minimum phase system, demonstrates effectiveness of the proposed method.
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 callMore From: Transactions of the Society of Instrument and Control Engineers
Disclaimer: 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.
