Abstract
A TSK-type recurrent fuzzy network (TSKRFN) control system is proposed to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories in this study. The proposed TSKRFN combines the merits of self-constructing fuzzy neural network (SCFNN), TSK-type fuzzy inference mechanism, and recurrent neural network (RNN). Moreover, the structure and the parameter learning phases are preformed concurrently and online in the TSKRFN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-descent method using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviour of the proposed TSKRFN control system is robust with regard to uncertainties.
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.
