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State Observer based Elman Recurrent Neural Network for Electric Drive of Optical-Mechanical Complexes

Belov, M.P. and Van Lanh, N. and Khoa, T.D. (2021) State Observer based Elman Recurrent Neural Network for Electric Drive of Optical-Mechanical Complexes. In: 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021, 26 January 2021 through 28 January 2021.

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Abstract

This paper proposes an application of Elman recurrent neural networks as state observer to estimate electromechanical variable coordinates in electric drive control system of the optical-mechanical complex. The mathematical description of electric drive of the optical-mechanical complex is developed in the form of a two-mass elastic system. These elastic vibrations can be damped by using additional feedback signals from the elastic moment and the load velocity. The architecture of dynamic recurrent neural networks-based Elman scheme in investigated in the form of vector-matrix model, which allows approximating a wide class of nonlinear dynamic systems. During computer simulation in the MATLAB/Simulink environment, the comparison of the root-mean-square error between different learning algorithms for Elman's recurrent neural networks was carried out to study their accuracy estimates coordinates in a closed loop control system of optical-mechanical complex. © 2021 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Control Engineering
Identification Number: 10.1109/ElConRus51938.2021.9396310
Uncontrolled Keywords: Closed loop control systems; Complex networks; Computer control systems; Dynamics; Elasticity; Electric drives; Estimation; Learning algorithms; MATLAB; Mean square error; Nonlinear dynamical systems; State estimation; Dynamic recurrent neural networks; Elastic vibration; Elman recurrent neural network; Elman's recurrent neural networks; Feedback signal; Mathematical descriptions; MATLAB/Simulink environment; Root mean square errors; Recurrent neural networks
Additional Information: Conference code: 168373. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8701

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