Synthesis of Robust Nonlinear Control Law Unsteady Dynamic Objects

An adaptive identifier for neuro-fuzzy control system nonlinear dynamic object operating in conditions of uncertainty intrinsic properties and the environment. The algorithms of structural and parametric identification in real time are a combination of an identification algorithm coefficients of linear management and methods of the theory of interactive adaptation. Adaptive neuro-fuzzy control system of nonlinear dynamic object contains an identifier and control that are based on Sugeno fuzzy model.This structure of the controller in conjunction with the optimal choice of the parameters of fuzzy controller, allows, at minimum settings, implement adaptive control systems uncertain and unsteady mechanisms regardless of their structure.To make the adaptive properties of fuzzy identifier proposed assessment rate of change of control error.Create a hybrid model based on neural networks and fuzzy models, improves the efficiency solution of the problem control of complex dynamic objects under uncertainty.