Abstract
In this paper, a self-constructing recurrent fuzzy neural network (SCRFNN) method is proposed to control the speed of a permanent-magnet synchronous motor to track periodic reference trajectories. The proposed SCRFNN combines the merits of self-constructing fuzzy neural network (SCFNN) and the recurrent neural network (RNN). The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-decent method. In addition, the Mahalanobis distance (M-distance) formula is employed that the neural network has the ability of identification of the neurons will be generated or not. Finally, the simulated results show that the control effort is robust.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.