Development of a method for automatic generation and optimization of fuzzy controller parameters using genetic algorithm

The method of automatic synthesis of a fuzzy controller and optimization of its parameters based on a genetic algorithm is developed. A distinctive feature of the method is an algorithm for processing statistical data about the operation of a real industrial facility, which makes it possible to form the initial knowledge base of a fuzzy controller (the number and type of membership functions used, the base of control rules). The use of a genetic algorithm allows optimizing the parameters of a fuzzy controller in such a way as to ensure the best quality indicators of its operation: the duration and oscillation of the transient process, the value of the steady-state error. The proposed method is automated due to the development of a special software application in the Matlab modeling environment, and requires minimal human participation in its work. Simulation modeling is carried out and results are presented that confirm the correctness of the proposed method and the possibility of its practical use. The method operation can be represented as a sequence of the following stages: forming the initial parameters of a fuzzy controller; searching for optimal lengths of term-sets of input-output linguistic variables; searching for optimal parameters of term-sets of input–output linguistic variables.