ADAPTIVE IDENTIFICATION OF A NEURAL SYSTEM FOR CONTROL OF NONLINEAR DYNAMIC OBJECTS

Abstract

An adaptive identifier is proposed for a neuro-fuzzy control system for a nonlinear dynamic object, operating under conditions of uncertainty of internal properties and external environment. Algorithms for structural and parametric identification in real time have been developed, which is a combination of an algorithm for identifying linear control coefficients and a method of interactive adaptation theory. An adaptive neuro-fuzzy system for controlling a nonlinear dynamic object, contains an identifier and a controller built on the basis of the Sugeno fuzzy model. This structure of the controller, combined with the optimal choice of parameters of the fuzzy controller, allows, with a minimum of settings, to implement adaptive control systems for uncertain and non -stationary mechanisms, regardless of their structure. To impart adaptive properties to the fuzzy identifier it is proposed to estimate the rate of change of error regulation The developed hybrid model, built on the basis of neural networks and fuzzy models, makes it possible to increase the efficiency of solving the problem of managing complex dynamic objects under conditions of uncertainty.