Robust recurrent fuzzy neural network control for linear synchronous motor drive system

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