Self-constructing recurrent fuzzy neural network for DSP-based permanent-magnet linear-synchronous-motor servodrive

Abstract

A self-constructing recurrent fuzzy-neural-network (SCRFNN) control system is proposed to control the position of the mover of a field-oriented control permanent-magnet linear-synchronous-motor (PMLSM) servodrive system to track periodic reference trajectories. The proposed SCRFNN combines the merits of self-constructing fuzzy neural network (SCFNN) and the recurrent neural network (RNN). Moreover, the structure and the parameter-learning phases are preformed concurrently and on-line in the SCRFNN. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-decent method using a delta-adaptation law. Further, 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 behaviors of the proposed SCRFNN control system are robust with regard to uncertainties.