Modified fuzzy adaptive controller applied to nonlinear systems modeled under quasi-ARX neural network

In this article, a fuzzy adaptive controller approach is presented for nonlinear systems. The proposed quasi-ARX neural network based on Lyapunov learning algorithm is used to update its weight for prediction model as well as to modify fuzzy adaptive controller. The improving performances of the Lya...

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Bibliographic Details
Published in:Artificial life and robotics 2014-02, Vol.19 (1), p.22-26
Main Authors: Sutrisno, Imam, Jami’in, Mohammad Abu, Hu, Jinglu
Format: Article
Language:English
Subjects:
Adaptive control systems
Artificial Intelligence
Computation by Abstract Devices
Computer Science
Computer simulation
Control
Fuzzy
Fuzzy logic
Fuzzy set theory
Learning
Mathematical models
Mechatronics
Neural networks
Robotics
Special Feature: Original Article
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Summary:In this article, a fuzzy adaptive controller approach is presented for nonlinear systems. The proposed quasi-ARX neural network based on Lyapunov learning algorithm is used to update its weight for prediction model as well as to modify fuzzy adaptive controller. The improving performances of the Lyapunov learning algorithm are stable in the learning process of the controller and able to increase the accuracy of the controller as well as fast convergence of error. The simulations are intended to show the effectiveness of the proposed method.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-013-0137-6