Fuzzy neural networks for identification and control of ultrasonic motor drive with LLCC resonant technique

Abstract

This paper demonstrates the applications of fuzzy neural networks (FNNs) in the identification and control of the ultrasonic motor (USM). First, the USM is derived by a newly designed high-frequency two-phase voltage-source inverter using LLCC resonant technique. Then, two FNNs with varied learning rates are proposed to control the rotor position of the USM. The USM drive system is identified by a fuzzy neural network identifier (FNNI) to provide the sensitivity information of the drive system to a fuzzy neural network controller (FNNC). A backpropagation algorithm is used to train both the FNNI and FNNC on-line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNs. In addition, the effectiveness of the FNN-controlled USM drive system is demonstrated by experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the FNNs. Furthermore, the influence of parameter variations and external disturbances on the USM drive system can be reduced effectively.