Title :
Vibration control of 2-mass system using a neural network torsional torque estimator
Author :
Song, Joong-ho ; Lee, Kyo-Beum ; Choy, Ick ; Kim, Kwang-Bae ; Choi, Joo-Yeop ; Lee, Kwang-Won
Author_Institution :
KIST, Seoul, South Korea
fDate :
31 Aug-4 Sep 1998
Abstract :
A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer based torque estimator with the low pass filter should adjust the time constant of the adopted filter according to the natural resonance frequency determined by considering the system parameters varied. The simulation results show the validity of the proposed control scheme
Keywords :
control system analysis; control system synthesis; motion control; neurocontrollers; parameter estimation; torque control; torsion; unsupervised learning; vibration control; control design; control performance; control simulation; motion control system; neural network; self-learning capability; speed vibration response; torsional torque estimator; two-mass system vibration control; Control systems; Frequency estimation; Low pass filters; Motion control; Motion estimation; Neural networks; Resonance; Resonant frequency; Torque control; Vibration control;
Conference_Titel :
Industrial Electronics Society, 1998. IECON '98. Proceedings of the 24th Annual Conference of the IEEE
Conference_Location :
Aachen
Print_ISBN :
0-7803-4503-7
DOI :
10.1109/IECON.1998.722960