Interval Type-2 Fuzzy Neural network Controller and Its Application in DC Motors

• Main Author: 潘建宏
• Other Authors: 洪欽銘
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/7926nu

碩士 === 國立臺灣師範大學 === 工業教育學系 === 98 === In this thesis, an adaptive backstepping interval Type-2 fuzzy neural network (IT2FNN) controller is proposed for a class of nonlinear system. We designed the controllers for affine and nonaffine nonlinear systems, respectively. The IT2FNN identifier is the main controller. The design of the controller can adjust its inside parameters, including mean and standard deviation. In order to adjust these parameters, we use adaptive law. We also use mean value theory to replace Taylor linearization expansion. Although Taylor linearization expansion, which can transform the nonlinear function into partially linear form. However, the linearization expansion method results in the fact that the higher-order derivative terms introduced into approximation model may produce the unpredictable and unfavorable influence on control performance. In addition, the stability of the closed-loop system is analyzed by mean of Lyapuniv function. Finally, simulation results use one example to demonstrate the output tracking error between the plant output and the desired reference command can achieve favorable tracking performance of the proposed scheme.