Fuzzy neural networks for identification and control of ultrasonicmotor drive with LLCC resonant technique
Faa-Jeng Lin; Rong-Jong Wai; Rou-Yong Duan
Industrial Electronics, IEEE Transactions on
Volume 46, Issue 5, Oct 1999 Page(s):999 - 1011
Digital Object Identifier 10.1109/41.793349
Summary: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
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