Abstract

This paper considers an iterative learning control (ILC) scheme for unknown linear discrete-time system. ILC updates the system input using error information from previous iteration to sequentially improve the tracking performance. The asymptotic convergence may not be guaranteed when the system model information is uncertain. In this paper, we introduce the adaptive Fourier decomposition (AFD) algorithm to deal with the system identification problem. This adaptive approximation algorithm estimates a system representation using input/output data only. The learning gain of ILC satisfying the convergence condition is obtained according to the estimated system parameters. The effectiveness of system approximation and the tracking performance of ILC based on input/output measurements are verified with simulation results.

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.