Abstract
This paper contributes to a point-to-point iterative learning control problem for stochastic systems without prior information on system matrices. The stochastic approximation technique with gradient estimation by random difference is introduced to design the update law for input. It is strictly proved that the input sequence would converge almost surely to the optimal one, which minimizes the averaged tracking performance index. An illustrative simulation shows the effectiveness of the proposed algorithm.
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: IEEE Transactions on Automation Science and Engineering
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.
