Recurrent neuro-fuzzy networks for nonlinear process modeling

Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive

In this study, a recurrent fuzzy neural network (RFNN) controller is proposed to control a piezoelectric ceramic linear ultrasonic motor (LUSM) drive system to track periodic reference trajectories with robust control performance. First, the structure and operating principle of the LUSM are described in detail. Second, because the dynamic characteristics of the LUSM are nonlinear and the precise dynamic model is difficult to obtain, a RFNN is proposed to control the position of the moving table of the LUSM to achieve high precision position control with robustness. The back propagation algorithm is used to train the RFNN on-line. Moreover, to guarantee the convergence of tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Then, the RFNN is implemented in a PC-based computer control system, and the LUSM is driven by a unipolar switching full bridge voltage source inverter using LC resonant technique. Finally, the effectiveness of the RFNN-controlled LUSM drive system is demonstrated by some experimental results. Accurate tracking response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the RFNN controller. Furthermore, the RFNN control system is robust with regard to parameter variations and external disturbances.