Neural-network-based adaptive control for induction servomotordrive system
Chih-Min Lin
Chun-Fei Hsu
Dept. of Electr. Eng., Yuan-Ze Univ., Chung-li ;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb 2002
Volume: 49,
Issue: 1
On page(s): 115-123
ISSN: 0278-0046
References Cited: 16
CODEN: ITIED6
INSPEC Accession Number: 7177969
Digital Object Identifier: 10.1109/41.982255
Current Version Published: 2002-08-07
Abstract
A neural-network-based adaptive control (NNAC) design method is
proposed to control an induction servomotor. In this NNAC design, a
neural network (NN) controller is investigated to mimic a feedback
linearization control law; and a compensation controller is designed to
compensate for the approximation error between the feedback
linearization control law and the NN controller. The interconnection
weights of the NN can be online tuned in the sense of the Lyapunov
stability theorem; thus, the stability of the control system can be
guaranteed. Additionally, in this NNAC system design, an error
estimation mechanism is investigated to estimate the bound of
approximation error so that the chattering phenomenon of the control
effort can be reduced. Simulation and experimental results show that the
proposed NNAC servomotor control systems can achieve favorable tracking
and robust performance with regard to parameter variations and external
load disturbances
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