DocumentCode :
995757
Title :
Hybrid controller with recurrent neural network for magnetic levitation system
Author :
Lin, Faa-Jeng ; Shieh, Hsin-Jang ; Teng, Li-Tao ; Shieh, Po-Huang
Author_Institution :
Dept. of Electr. Eng., Nat. Dong Hwa Univ., Hualien, Taiwan
Volume :
41
Issue :
7
fYear :
2005
fDate :
7/1/2005 12:00:00 AM
Firstpage :
2260
Lastpage :
2269
Abstract :
We propose a hybrid controller using a recurrent neural network (RNN) to control a levitated object in a magnetic levitation system. We describe a nonlinear dynamic model of the system and propose a computed force controller, based on feedback linearization, to control the position of the levitated object. To relax the requirement of the lumped uncertainty in the design of the computed force controller, an RNN functions as an uncertainty observer to adapt the lumped uncertainty on line. The computed force controller, the RNN uncertainty observer, and a compensated controller are embodied in a hybrid controller, which is based on Lyapunov stability. The computed force controller, with the RNN uncertainty observer, is the main tracking controller, and the compensated controller compensates the minimum approximation error of the RNN uncertainty observer. To ensure the convergence of the RNN, the adaptation law of the RNN is modified by using a projection algorithm. Experimental results illustrate the validity of the proposed control design for the magnetic levitation system.
Keywords :
Lyapunov methods; control system synthesis; error compensation; feedback; force control; linearisation techniques; magnetic levitation; nonlinear dynamical systems; position control; recurrent neural nets; Lyapunov stability; RNN; approximation error; compensated controller; computed force controller; feedback linearization; hybrid controller; lumped uncertainty; magnetic levitation system; nonlinear dynamic model; position control; projection algorithm; recurrent neural network; tracking controller; Control systems; Force control; Force feedback; Linear feedback control systems; Magnetic levitation; Neurofeedback; Nonlinear control systems; Nonlinear dynamical systems; Recurrent neural networks; Uncertainty; Computed force controller; hybrid controller; magnetic levitation system; recurrent neural network;
fLanguage :
English
Journal_Title :
Magnetics, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9464
Type :
jour
DOI :
10.1109/TMAG.2005.848320
Filename :
1463288
Link To Document :
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