TSK-type recurrent fuzzy network for DSP-based permanent-magnet linear synchronous motor servo drive

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.