NEURO-ADAPTIVE CONTROL SYSTEM FOR NONLINEAR TECHNOLOGICAL OBJECTS
Keywords:
Key words: nonlinear dynamic object, neuron-fuzzy identification, interactive adaptation, training, fuzzy logic, neural network, model.Abstract
Abstract: An adaptive identifier is proposed for a neuron-fuzzy control system of a nonlinear dynamic object that operates under conditions of uncertainty of internal properties and the external environment. Algorithms of structural and parametric identification in real time, which is a combination of the algorithm for identifying the coefficients of linear controls and the method of the theory of interactive adaptation, are developed. Adaptive neuron-non-linear control system for a nonlinear dynamic object, contain an identifier and a regulator built on the basis of the fuzzy Sugeno model. Such a regulator structure in combination with the optimal choice of fuzzy controller parameters allows, with a minimum of settings, to implement adaptive control systems of undefined and non-stationary mechanisms regardless of their structure. To impart adaptive properties to the fuzzy identifier, it is suggested to estimate the rate of change of the control error. The developed hybrid model built on the basis of neural networks and fuzzy models, it makes it possible to improve the efficiency of the task of managing complex dynamics kimi objects under uncertainty.
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