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 non-linear processes to show the effectiveness of the proposed scheme.