Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Marcin Kamiński and Krzysztof Szabat
Additional contact information
Marcin Kamiński: Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland
Krzysztof Szabat: Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland

Energies, 2021, vol. 14, issue 12, 1-26

Abstract: This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Keywords: recurrent neural network; ADALINE; signal filtering; signal processing; speed control; two-mass system; electrical drive (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/12/3389/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/12/3389/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:12:p:3389-:d:571311

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().