Abstract

In this paper, finite-time identification of discrete-time nonlinear system is studied without the persistence of excitation requirement. A finite-time learning method is introduced to learn the uncertainties of the discrete-time nonlinear systems’ dynamics that employs a memory stack of experienced data fulfilling an easy-to-check rank condition. The proposed method assures the convergence of the estimated parameters in finite time based on a Lyapunov analysis. Finally, simulation results demonstrate the effectiveness of the proposed method in comparison with the existing methods.

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 call

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.