Decentralized PID neural network control for five degree-of-freedom active magneticbearing

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/106861

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106861

Title:	Decentralized PID neural network control for five degree-of-freedom active magneticbearing
Authors:	林法正;Chen, Syuan-Yi;Lin, Faa-Jeng
Contributors:	資訊電機學院電機工程學系
Keywords:	Active control;Active magnetic bearing;Control systems;Decentralized;Decentralized control;Degrees of freedom;Dynamical systems;Gradient descent method;Mathematical analysis;Neural networks;Nonlinear dynamics;PID neural network
Date:	2013-03-01
Issue Date:	2026-04-23 13:46:56 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： A decentralized proportional–integral–derivative neural network (PIDNN) control scheme is proposed to regulate and stabilize a fully suspended five degree-of-freedom (DOF) active magnetic bearing (AMB) system which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB). First, the structure and operating principles of the five-DOF AMB system are introduced. Then, the adopted differential driving mode (DDM) for the drive system of the AMB is analyzed. Moreover, due to the exact dynamic model of the five-DOF AMB system is vague, a decentralized PIDNN controller is proposed to control the five-axes of the rotor simultaneously in order to confront the uncertainties including inherent nonlinearities and external disturbances effectively. Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function. Based on the decentralized concepts, the computational burden is reduced and the controller design is simplified. Finally, the experimental results show that the proposed control scheme provides good control performances and robustness for controlling the fully suspended five-DOF AMB system in different operating conditions. 出版者： Elsevier Ltd 出版日期： 2013-03 出處： Engineering applications of artificial intelligence, 2013-03, Vol.26 (3), p.962-973 版權： 2012 Elsevier Ltd 識別號： ISSN: 0952-1976 識別號： EISSN: 1873-6769 識別號： DOI: 10.1016/j.engappai.2012.11.002
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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