SIA OpenIR  > 工业控制网络与系统研究室
DSP implementation of a neural network vector controller for IPM motor drives
Sun, Yang1; Li SH(李署辉)1; Ramezani, Malek1; Balasubramanian, Bharat2; Bian J(卞晶)3; Gao, Yixiang1
Department工业控制网络与系统研究室
Source PublicationEnergies
ISSN1996-1073
2019
Volume12Issue:13Pages:1-17
Indexed BySCI ; EI
EI Accession number20192907187209
WOS IDWOS:000477034700098
Contribution Rank3
Funding OrganizationCenter of Advanced Vehicle Technology, The University of Alabama
Keywordpermanent-magnet synchronous motor (PMSM) vector control approximate dynamic programming (ADP) artificial neural network (ANN) digital signal processor (DSP) implementation real-time control linear and over-modulation
AbstractThis paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions.
Language英语
WOS SubjectEnergy & Fuels
WOS KeywordADAPTIVE CRITICS ; PMSM ; PWM ; OVERMODULATION ; TORQUE
WOS Research AreaEnergy & Fuels
Funding ProjectCenter of Advanced Vehicle Technology, The University of Alabama ; Center of Advanced Vehicle Technology, The University of Alabama
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25315
Collection工业控制网络与系统研究室
Corresponding AuthorLi SH(李署辉)
Affiliation1.Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35401, United States
2.Center for Advanced Vehicle Technologies, University of Alabama, Tuscaloosa, AL 35401, United States
3.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Sun, Yang,Li SH,Ramezani, Malek,et al. DSP implementation of a neural network vector controller for IPM motor drives[J]. Energies,2019,12(13):1-17.
APA Sun, Yang,Li SH,Ramezani, Malek,Balasubramanian, Bharat,Bian J,&Gao, Yixiang.(2019).DSP implementation of a neural network vector controller for IPM motor drives.Energies,12(13),1-17.
MLA Sun, Yang,et al."DSP implementation of a neural network vector controller for IPM motor drives".Energies 12.13(2019):1-17.
Files in This Item: Download All
File Name/Size DocType Version Access License
DSP implementation o(3701KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Yang]'s Articles
[Li SH(李署辉)]'s Articles
[Ramezani, Malek]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Yang]'s Articles
[Li SH(李署辉)]'s Articles
[Ramezani, Malek]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Yang]'s Articles
[Li SH(李署辉)]'s Articles
[Ramezani, Malek]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: DSP implementation of a neural network vector controller for IPM motor drives.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.