Neural network based intelligent learning of fuzzy logic controller parameters

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately. Copyright © 2004 by ASME.

Duke Authors

Cited Authors

  • Kumar, M; Garg, DP

Published Date

  • 2004

Published In

  • American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC

Volume / Issue

  • 73 / 1 PART A

Start / End Page

  • 625 - 634