Development of a fuzzy stabilization control intelligent system under conditions of input influences uncertainty

Abstract. The problems of complex control systems functioning under conditions of input influences uncertainty are discussed. Existing approaches to solving these problems, based on the use of fuzzy control methods, adaptive control and intelligent algorithms, are described. The synthesis of an intelligent system with fuzzy stabilization control under conditions of input influences uncertainty is carried out to increase the efficiency of the object's functioning. The use of fuzzy control makes it possible to take into account the input influences uncertainty and stabilize control based on a production model of knowledge representation, which makes the system more flexible and resistant to change. Optimal defuzzification method dynamic selection for the purpose of fuzzy stabilization control ensures the effective functioning of each specific system. An example is considered that implements an intelligent system with fuzzy stabilization of electric current control for an electroplating process with uncertainty about its duration, part area, temperature and acidity of the electrolyte. To confirm the effectiveness of the developed intelligent system, a computational experiment is carried out on the example of controlling the electroplating process of applying nickel coating in a Watts electrolyte using fuzzy inference by the Mamdani algorithm with the triangular norm and the Zadeh conorm. The results show that using an intelligent system with fuzzy stabilization control leads to a more accurate result (in terms of coating thickness) compared to using the most common defuzzification methods (centroid; bisector; smallest, middle and largest of maximum) on your own.