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http://dx.doi.org/10.5391/IJFIS.2008.8.1.068

Neural Network Compensation Technique for Standard PD-Like Fuzzy Controlled Nonlinear Systems  

Song, Deok-Hee (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
Lee, Geun-Hyeong (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
Jung, Seul (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.8, no.1, 2008 , pp. 68-74 More about this Journal
Abstract
In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.
Keywords
Inverted pendulum; fuzzy logic controller; neural network controller; FEL; RCT;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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