Neuro-fuzzy Control System for a Non-deterministic Object in Real Time
Sergey Ivanov, Nataliia Maksyshko, Mykola Ivanov · 2021
In this paper discusses the use of neuro-fuzzy control systems as a tool for managing nondeterministic objects in real time.This paper discusses modern control tools structural models of a discrete quasi-invariant automated control system.In this paper the analysis of an automated control system is presented, which is based on the use of typical models of discrete automated control systems.According to the proposed solution in the automated control system in real time it is proposed to use a neuro-fuzzy control system as a function of the object and the system's transfer ratio.The neuro-fuzzy control system is based on the learning process of an artificial neural network (ANN), which allows to define the rules of fuzzy inference (FIS).The paper proposes the ANFIS model, which is implemented by using the fuzzy system Takagi T., Sugeno M., also is considered an algorithm based on seven fuzzy rules.In this paper is presented a technique for implementing a neuro-fuzzy control system for non-deterministic objects by using Matlab.The use of Matlab made possible to create a model of an adaptive neuro-fuzzy inference system.The paper describes the process of training a neural network, where a hybrid method is chosen, which is a combination of the least squares method and the method of decreasing the inverse gradient.The results of testing a neuro-fuzzy control system of a non-deterministic object are presented, which confirmed the possibility of using a neuro-fuzzy control model.It is constructed the structure and the result in the form of a control surface of the neuro-fuzzy ANFIS model.The results presented in this paper allow to conclude about the possibility of using a neuro-fuzzy control system for non-deterministic objects in real time.