단결정 실리콘의 파괴거동을 위한 초정밀 위치결정시스템의 개발

Abstract

본 ?П맙【?는 신경회로망 제어기와 PID제어기의 장점을 이용한 신경회로망-PID 병렬제어기를 이용하여 압전구동시스템의 동적 모형이나 히스테리시스 모델을 구해야 하는 과정을 생략하였고, 전체 구동시스템의 동특성을 실시간적으로 자기학습(self-learning) 시켜서 히스테리스와 같은 비선형문제를 보상하도록 하였다. 그리고, 실제실험을 통하여 제안된 신경회로망-PID 제어기가 만족할만한 위치제어성능을 보임을 검증하였으며 개발된 장치를 이용하여 단결정 실리콘의 압입시험을 수행하였다.

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement.

In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the begining of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural network controller.

In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID controller.

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement.

In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the begining of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural network controller.

In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID controller.