Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

Radoslaw Stanislawski, Jules-Raymond Tapamo and Marcin Kaminski ()
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Radoslaw Stanislawski: Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland
Jules-Raymond Tapamo: School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa
Marcin Kaminski: Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland

Energies, 2023, vol. 16, issue 15, 1-23

Abstract: Neural network approaches have commonly been used to solve complex mathematical equations in the literature. They have inspired the modifications of state controllers and are often implemented for electrical drives with an elastic connection. Given that the addition of a virtual signal can provide adaptive properties to classical controllers and that selected feedback signals can also be replaced with a virtual state variable from a neural network, several combinations can be considered and compared. In this paper, R adial B asis F unction neural-network-based control algorithms are proposed in which online updating of the output weights is used. Analyses of simulation experiment results reveal that the proposed control algorithms significantly improve the operation of classic-state feedback controllers applied to two-mass systems in the presence of parameter uncertainty.

Keywords: neural signal generation; radial basis function neural network; control with additional feedback; two-mass system; adaptive system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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