NDLI: On-line identification and compensation-based model reference adaptive neural network speed control for LPMSM

Content Provider	IEEE Xplore Digital Library
Author	Guo Qingding Xie Dongmei
Copyright Year	2002
Description	Author affiliation: Sch. of Electr. Eng., Shenyang Univ. of Technol., China (Guo Qingding; Xie Dongmei)
Abstract	Under the condition that the object model is definite, unvaried and linear, and that the operation condition and environment are ascertained, it is efficient to adopt a traditional control scheme. But for the situation of high precision and fine feed, the timing and variable factors such as the variation of structure and parameters of the object, the effects of all kinds of nonlinear factors, the changes of operation environment and environmental interferes must be considered to obtain a satisfying control effect. Nowadays modern control schemes are taken seriously in the study of LPMSM. In this paper, the authors propose a control scheme for the speed servo control of LPMSM with model reference adaptive and neural network techniques. Model reference adaptive neural network control is a new technique which is the combination of model reference adaptive control and neural network control. This kind of control method has double the advantages of the two methods. To improve the robustness of the system, the authors use an online identification technique to compensate the variation of the parameters and modify the calculation of the neural networks teacher value. The results of simulation show that the system has good speed servo performance, especially in overcoming the end effects of LPMSM.
Sponsorship	IEEE Ind. Electron. Soc. Univ. Maribor Slovenia Ministr. Educ., Sci. & Sport Soc. Instrum. & Control Eng. (Japan) IEEE Region 8, Slovenia Sect
Starting Page	322
Ending Page	325
File Size	305718
Page Count	4
File Format	PDF
ISBN	0780374797
DOI	10.1109/AMC.2002.1026939
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-03
Publisher Place	Slovenia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Programmable control Adaptive control Neural networks Velocity control Feeds Timing Servosystems Robustness Servomechanisms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Model reference adaptive fuzzy neural network control based speed servo system of linear permanent magnet synchronous motor

CMAC-based model reference adaptive speed control using in high precision servo systems

A Neural-Network-Based Model Reference Speed Control for High Precision Motion Control Systems

Reinforcement adaptive learning neural network based friction compensation for high speed and precision

Friction compensation based on improved performance model reference adaptive control

Rotor Speed Identification of Doubly-Fed Generator System Based on Neural Network

Model reference adaptive PID control of hydraulic parallel robot based on RBF neural network

Model reference adaptive control with neural network for electro-pneumatic servo system

Adaptive PID control and on-line identification for switched reluctance motors based on BP neural network

On-line identification and compensation-based model reference adaptive neural network speed control for LPMSM

Similar Documents

Model reference adaptive fuzzy neural network control based speed servo system of linear permanent magnet synchronous motor

CMAC-based model reference adaptive speed control using in high precision servo systems

A Neural-Network-Based Model Reference Speed Control for High Precision Motion Control Systems

Reinforcement adaptive learning neural network based friction compensation for high speed and precision

Friction compensation based on improved performance model reference adaptive control

Rotor Speed Identification of Doubly-Fed Generator System Based on Neural Network

Model reference adaptive PID control of hydraulic parallel robot based on RBF neural network

Model reference adaptive control with neural network for electro-pneumatic servo system

Adaptive PID control and on-line identification for switched reluctance motors based on BP neural network

On-line identification and compensation-based model reference adaptive neural network speed control for LPMSM