• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.362-362
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• 2000

The purpose of this thesis is to reduce force ripple of linear pulse motor(LPM) using neural network and to enhance precision. In order to this, we propose a new controller using a neural network to compensate disturbances. The structure includes adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances. The proposed controller compensates an unmodeled dynamics in the LPM. The neural network changes a current command to reduce position error and force ripple of the LPM. We compare proposed controller with PI controller. Simulation result shows that the proposed controller has better performance than a PI controller without neural network.