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Khalmatov, Davronbek
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Computer Science & IT Electrical & Electronics Engineering

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Neural network control of a nonlinear dynamic plant with a predictive model Siddikov, Isamidin; Khalmatov, Davronbek; Khushnazarova, Dilnoza; Khujanazarov, Ulugbek
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5131-5138

Abstract

The paper considered the possibilities of applications of neural network technologies to control a dynamic plant with nonlinear properties. To give the control system the desired dynamic property, the use of a neural network predictive controller is proposed. The model of the control plant is in the form of a multilayer forward-directional neural network, which allows us to construct a controller using generalized equation methods with prediction. A neural network control algorithm with prediction based on minimizing the quadratic quality functional is proposed. The algorithm makes it possible to minimize the root mean square error of regulation and the control signal rate of change. To determine the sequences of optimal control impacts, the application of the Newton-Raphson method is proposed. To reduce computational costs when receiving control signals, the decomposition of the original matrix, represented as a Hess matrix, is carried out. To predict the behavior of a control plant, a formula is proposed for calculating the gradient of a neural network, discrepant by the possibility of its use in the real-time mode of the control. The proposed algorithm of the neural network control with predictive allows higher quality control of complex nonlinear dynamic plants in the real-time mode.
Investigation of auto-oscilational regimes of the system by dynamic nonlinearities Siddikov, Isamidin; Khalmatov, Davronbek; Alimova, Gulchekhra; Khujanazarov, Ulugbek; Feruzaxon, Sadikova; Usanov, Mustafaqul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp230-238

Abstract

The paper proposes a method for the analysis and synthesis of self-oscillations in the form of a finite, predetermined number of terms of the Fourier series in systems reduced to single-loop, with one element having a nonlinear static characteristic of an arbitrary shape and a dynamic part, which is the sum of the products of coordinates and their derivatives. In this case, the nonlinearity is divided into two parts: static and dynamic nonlinearity. The solution to the problem under consideration consists of two parts. First, the parameters of self-oscillations are determined, and then the parameters of the nonlinear dynamic part of the system are synthesized. When implementing this procedure, the calculation time depends on the number of harmonics considered in the first approximation, so it is recommended to choose the minimum number of them in calculations. An algorithm for determining the self-oscillating mode of a control system with elements that have dynamic nonlinearity is proposed. The developed method for calculating self-oscillations is suitable for solving various synthesis problems. The generated system of equations can be used to synthesize the parameters of both linear and nonlinear parts. The advantage is its versatility.
Synergetic synthesis of a neural network controller for an adaptive control of a nonlinear dynamic plant Siddikov, Isamidin; Khalmatov, Davronbek; Iskandarov, Zokhid; Khushnazarova, Dilnoza
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5258-5265

Abstract

The paper considered issues the development of a self-organizing controller (SC) based on a neuro-fuzzy network that can approximate a nonlinear function with arbitrary accuracy. The SC in the form of neuro-fuzzy networks, possesses the nonlinear property that allows for an increased range of control over the plant, which imparts adaptive properties to the control systems. To reduce the dimensionality of the plant, it is proposed to split the model of the system into sub models with smaller dimensionality, due to which the duration of training of the neuro-fuzzy network is reduced and asymptotic stability is ensured as a whole. The proposed approach is also applicable to multidimensional control systems of the nonlinear dynamic plants. The simulation results showed that the synthesized SC provides good tracking characteristics, the tracking efficiency is no more than 10%, which meets the requirement of the control system.
Co-Authors Alimova, Gulchekhra Feruzaxon, Sadikova Iskandarov, Zokhid Khujanazarov, Ulugbek Khushnazarova, Dilnoza Siddikov, Isamidin Usanov, Mustafaqul