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The presented paper is devoted to the development of an adaptive control system for a
nonlinear dynamic object using neuro-fuzzy networks. The authors note that in real conditions dynamic
objects function under conditions of uncertainty, which are characterised by complex and poorly
understood relationships between technological variables, the presence of perturbing and random
disturbances, as well as nonlinear elements. This makes it difficult to apply traditional linear adaptive
control algorithms. The use of self-organising adaptive control is proposed, in which the mathematical
model of the controlled system is formed by operational identification during system operation. It is
shown that linear MRAC (Model Reference Adaptive Control) controllers work well only in the
neighbourhood of the operating point where the system can be approximated by a linear model. For
effective control of nonlinear systems, it is proposed to use neuro-fuzzy networks that can approximate
any nonlinear function with arbitrary accuracy. The developed nonlinear MRAC controller based on
neuro-fuzzy networks allows smooth transition between different operating points, unlike traditional
self-tuning controllers. The online updating of the weights of the neuro-fuzzy network provides local
stability of the closed-loop control system. The proposed approach can find application in the control
of various nonlinear dynamic objects.
 

  • Web Address
  • DOI https://doi.org/10.59048/2181-1180.1598
  • Date of creation in the UzSCI system 16-05-2025
  • Read count 17
  • Date of publication 07-05-2025
  • Main LanguageIngliz
  • Pages43-48
English

The presented paper is devoted to the development of an adaptive control system for a
nonlinear dynamic object using neuro-fuzzy networks. The authors note that in real conditions dynamic
objects function under conditions of uncertainty, which are characterised by complex and poorly
understood relationships between technological variables, the presence of perturbing and random
disturbances, as well as nonlinear elements. This makes it difficult to apply traditional linear adaptive
control algorithms. The use of self-organising adaptive control is proposed, in which the mathematical
model of the controlled system is formed by operational identification during system operation. It is
shown that linear MRAC (Model Reference Adaptive Control) controllers work well only in the
neighbourhood of the operating point where the system can be approximated by a linear model. For
effective control of nonlinear systems, it is proposed to use neuro-fuzzy networks that can approximate
any nonlinear function with arbitrary accuracy. The developed nonlinear MRAC controller based on
neuro-fuzzy networks allows smooth transition between different operating points, unlike traditional
self-tuning controllers. The online updating of the weights of the neuro-fuzzy network provides local
stability of the closed-loop control system. The proposed approach can find application in the control
of various nonlinear dynamic objects.
 

№ Name of reference
1 Fergana branch of Tashkent University of Information Technologies
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