A permanent-magnet synchronous motor servo drive using self-constructing fuzzy neural network controller

A self-constructing fuzzy neural network (SCFNN) is proposed to control the rotor position of a permanent-magnet synchronous motor (PMSM) drive to track periodic step and sinusoidal reference inputs in this study. The structure and the parameter learning phases are preformed concurrently and online in the SCFNN. 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. Several simulation and experimental results are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem under the occurrence of parameter variations and external disturbance.