An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

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