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

Abstract:

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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 structure and membership parameters are organized as real value chromosomes, and the chromosomes are trained by the reinforcement learning based on genetic algorithm. This paper uses Matlab/Simulink to establish simulation platform and 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.

Info:

Periodical:

Advanced Materials Research (Volumes 139-141)

Edited by:

Liangchi Zhang, Chunliang Zhang and Tielin Shi

Pages:

1763-1768

DOI:

10.4028/www.scientific.net/AMR.139-141.1763

Citation:

Q. Wang et al., "Reinforcement Learning Based Self-Constructing Fuzzy Neural Network Controller for AC Motor Drives", Advanced Materials Research, Vols. 139-141, pp. 1763-1768, 2010

Online since:

October 2010

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Price:

$35.00

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