Reinforcement Learning Based Self-Constructing Fuzzy Neural Network Controller for AC Motor Drives

被引：0

作者：

Zhao Jin ^{[1
]}

Wang Jianjing ^{[1
]}

Zhang Huajun ^{[1
]}

Yang Wei ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan, Peoples R China

来源：

2011 6TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2011年

关键词：

self-constructing; fuzzy neural network; reinforcement learning; genetic algorithm; SYSTEMS; LOGIC;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A self-constructing fuzzy neural network (SCFNN) based on reinforcement learning is proposed in this study. In the SCFNN, structure and parameter learning are implemented simultaneously. Structure learning is based on uniform division of the input space and distribution of membership function. The parameters are trained by the reinforcement learning based on genetic algorithm. Several simulations are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of AC motor speed drive. The simulation results show that the AC drive system with SCFNN has good anti-disturbance performance while the load change randomly.

引用

页码：913 / 918

页数：6

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