Neural-Network Application for Mechanical Variables Estimation of a Two-Mass Drive System

Author

Orlowska-Kowalska, Teresa ; Szabat, Krzysztof

Author_Institution

Inst. of Electr. Machines, Drives & Measurements, Tech. Univ. Wroclaw

Volume

54

Issue

3

fYear

2007

fDate

6/1/2007 12:00:00 AM

Firstpage

1352

Lastpage

1364

Abstract

This paper deals with the application of neural networks (NNs) to the mechanical state estimation of the drive system with elastic joint. The torsional vibrations of the two-mass system are damped using the control structure with additional feedbacks from the torsional torque and the load-side speed. These feedbacks signals are obtained using NN estimators. The learning procedure of the NNs is described, and the influence of the input vector size to the accuracy of the state-variable estimation is investigated. The neural estimators of the torsional torque and the load machine speed are tested with open-loop and closed-loop control structures. The simulation results are confirmed by laboratory experiments

Keywords

closed loop systems; control engineering computing; damping; drives; elasticity; learning (artificial intelligence); mechanical engineering computing; neurocontrollers; open loop systems; state estimation; torque; vibrations; closed-loop control; damping; elastic joint; learning; mechanical state-variable estimation; neural-networks; open-loop control; torsional torque feedback; torsional vibration; two-mass drive system; Control systems; Laboratories; Mechanical variables control; Neural networks; Neurofeedback; Open loop systems; State estimation; Testing; Torque control; Vibration control; Neural networks (NNs); state variable estimation; torsional vibration; two-mass system;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/TIE.2007.892637

Filename

4148045