Self-tuning fuzzy controller design using genetic optimisation and neural network modelling

This article describes a new adaptive fuzzy logic control scheme. The proposed scheme is based on the structure of the self-tuning regulator and employs neural network and genetic algorithm techniques. The system comprises two main parts: on-line process identification and fuzzy logic controller modification using the identified model. A recurrent neural network performs the identification and a genetic algorithm obtains the best process model and evolves the best controller design. The paper presents simulation results for linear and nonlinear processes to show the effectiveness of the proposed scheme. 1999 Elsevier Science Ltd. All rights reserved.

• Type: Journal
• Year: 1999
• Where: AEI
• Authors: Duc Truong Pham, Dervis Karaboga
• Keywords: AEI 1999, Fuzzy Logic, Genetic Algorithm, Neural Network