Abstract
This paper presents higher-order adaptive iterative learning control for trajectory tracking of uncertain robot manipulators. The proposed control schemes have been given rigorous proof of convergence under some assumptions. The schemes are based upon the use of a proportional-derivative (PD) feedback structure, for which an iterative term is added to cope with the unknown parameters and disturbances. Higher-order adaptive iterative learning control has potential to give a better convergence performance than the first-order scheme algorithms ,because of using past system control information from more than one past iterative cycle. The effectiveness of the proposed method is shown through numerical simulation results.
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.
