Fuzzy neural networks for identification and control of ultrasonicmotor drive with LLCC resonant technique
Faa-Jeng Lin
Rong-Jong Wai
Rou-Yong Duan
Dept. of Electr. Eng., Chung Yuan Christian Univ., Chung Li;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Oct 1999
Volume: 46,
Issue: 5
On page(s): 999-1011
ISSN: 0278-0046
References Cited: 30
CODEN: ITIED6
INSPEC Accession Number: 6383642
Digital Object Identifier: 10.1109/41.793349
Current Version Published: 2002-08-06
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
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
