Abstract
An efficient neural network based learning control scheme is proposed to solve the trajectory tracking controI problem of robot manipulators. The proposed approach has four distinctive characteristics: 1) good tracking performance can be achieved during the first learning trial; 2) learning algorithm for adjusting neural network weights is independent of the manipulator dynamic model, thus displays strong robustness to torque disturbances and model parameter uncertainty; 3) no acceleration measurement or estimation is needed; and 4) real-time implementation with a higher sampling rate is readily possible. Simulation results on a 3 degree-of-freedom manipulator are presented to show its validity.
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.
