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A linear synchronous motor drive using robust fuzzy neural network control

A robust fuzzy neural network (RFNN) control system is proposed to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories in this study. In the proposed RFNN control system, a FNN controller is the main tracking controller, which is used to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. The ideal feedback linearization control law is designed based on the backstepping technique. Moreover, to relax the requirement for the bound of uncertainty, which comprises a minimum approximation error, optimal parameter vectors and higher-order terms in Taylor series, a RFNN control system with adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.