Abstract
This paper proposes a motion learning method for motion systems using basis function networks. Motion systems have unknown nonlinearities such as variation of inertia, Coriolis's force, nonlinear electromagnetic driving characteristics and gravity that are complicated to formulate. In this study, these nonlinearities are described by basis function networks. The updating rules of the weights in the network are derived by Popov's Hyper Stable Theory. However, there exist modeling errors and unmodeled forces which generate tracking errors. Therefore, H∞control is introduced to attenuate the influence of modeling errors. This paper shows an analytical formulation of basis function networks for motion systems and effectiveness of the proposed motion control method by demonstrating an experiment of tracking desired trajectory in 2 types of 2-degree-of-freedom link motion system that have different motion equations.
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: TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C
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.
