Neural-network-based self-tuning PI controller for precise motioncontrol of PMAC motors
Gou-Jen Wang
Chuan-Tzueng Fong
Chang, K.J.
Dept. of Mech. Eng., Nat. Chung-Hsing Univ., Taichung;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Apr 2001
Volume: 48,
Issue: 2
On page(s): 408-415
ISSN: 0278-0046
References Cited: 22
CODEN: ITIED6
INSPEC Accession Number: 6901635
Digital Object Identifier: 10.1109/41.915420
Current Version Published: 2002-08-07
Abstract
In general, proportional plus integral (PI) controllers used in
computer numerically controlled machines possess fixed gain. They may
perform well under some operating conditions, but not all. To increase
the robustness of fixed-gain PI controllers, we propose a new
neural-network-based self-tuning PI control system. In this new
approach, a well-trained neural network supplies the PI controller with
suitable gain according to each operating condition pair (torque,
angular velocity, and position error) detected. To demonstrate the
advantages of our proposed neural-network-based self-tuning PI control
technique, both computer simulations and experiments were executed in
this research. During the computer simulation, the direct experiment
method was adopted to better model the problem of hysteresis in the AC
servo motor. In real experiments, a PC-based controller was used to
carry out the control tasks. Results of both computer simulations and
experiments show that the newly developed dynamic PI approach
outperforms the fixed PI scheme in rise time, precise positioning, and
robustness
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