Abstract
A robust adaptive control based on orthogonal neural network (ONN) is presented for robot manipulators in this paper. The adaptive controller constructed by Legendre orthogonal function neural network has some advantages such as simple structure and fast convergence speed. The adaptive learning law of orthogonal neural network is derived to guarantee that the adaptive weight errors and tracking errors are bounded by using Lyapunov stability theory. Simulation results on a two-link robot manipulator validate the control scheme.
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: International Journal of Modelling, Identification and Control
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.
