DocumentCode
848642
Title
Neural Network Saturation Compensation for DC Motor Systems
Author
Jang, Jun Oh
Author_Institution
Dept. of Comput. Control Eng., Uiduk Univ., Kyongju
Volume
54
Issue
3
fYear
2007
fDate
6/1/2007 12:00:00 AM
Firstpage
1763
Lastpage
1767
Abstract
A neural network (NN) saturation compensation scheme for dc motor systems is presented. The scheme, which leads to stability, command following, and disturbance rejection, is rigorously proven. The online weight tuning law, overall closed-loop performance, and boundness of the NN weights are derived and guaranteed based on the Lyapunov approach. Simulation and experimental results show that the proposed scheme effectively compensates for saturation nonlinearity in the presence of system uncertainty
Keywords
DC motors; Lyapunov methods; neural nets; stability; DC motor systems; Lyapunov approach; closed-loop performance; neural network saturation compensation; online weight tuning law; stability; Actuators; Control systems; DC motors; Hysteresis; Neural networks; Neurons; Nonlinear control systems; Nonlinear systems; Stability; Windup; Actuator nonlinearity; dc motor system; neural networks (NNs); saturation compensation; stability;
fLanguage
English
Journal_Title
Industrial Electronics, IEEE Transactions on
Publisher
ieee
ISSN
0278-0046
Type
jour
DOI
10.1109/TIE.2007.894706
Filename
4200883
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