NDLI: Torque–flux linkage recurrent neural network adaptive inversion control of torque for switched reluctance motor

Content Provider	IET Digital Library
Author	Dang, Xuanju Shi, Yazhou Peng, Huimin
Abstract	In order to reduce the torque ripple for switched reluctance motor (SRM), the learning error preprocessing-based torque–flux linkage recurrent neural network adaptive inversion control (TFRNNAIC) for SRM torque is proposed by the filter preprocessing and the non-linear mechanism characteristics for SRM. In TFRNNAIC with the advantages of parallel and series–parallel structure, the torque feedback error learning method is employed to update the weights of the torque–flux linkage recurrent neural network. To suppress the ripple effectively in torque error used for the weight learning of the torque–flux linkage recurrent neural network and obtain an accurate TFRNNAIC, namely the torque–flux linkage model for SRM, the low-pass filter preprocessing for the torque error is used. Moreover, the other low-pass filter is executed to reduce the ripple in the output for the PD torque control. The superposition of outputs for TFRNNAIC and PD torque control is taken as the reference flux linkage. Compared with other control strategies, such as the classical parallel neural network control, the classical series–parallel neural network control and TFRNNAIC, the simulation results show that the learning error preprocessing-based TFRNNAIC for SRM torque is capable of effectively reducing the torque ripple for SRM with the good recurrent performance.
Starting Page	1612
Ending Page	1623
Page Count	12
ISSN	17518660
Volume Number	14
e-ISSN	17518679
Issue Number	Issue 9, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-epa/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-epa.2020.0105
Journal	IET Electric Power Applications
Publisher Date	2020-05-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Control Classical Series–parallel Neural Network Control Control of Electric Power System Feedback Filter Preprocessing Learning Error Preprocessing-based TFRNNAIC Learning Error Preprocessing-based Torque–flux Linkage Recurrent Neural Network Adaptive Inversion Control Learning System Low-pass Filter Low-pass Filter Preprocessing Machine Control Mechanical Variables Control Neurocontrol Neurocontrollers Nonlinear Mechanism Characteristic PD Control PD Torque Control Recurrent Neural Nets Reference Flux Linkage Reluctance Motors Self Adjusting Control System Series–parallel Structure SRM Torque Switched Reluctance Motor Synchronous Machine Torque Control Torque Feedback Error Learning Method Torque Ripple
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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