불평형부하 구동용 유도전동기의 적응속도 제어

Abstract

Vector control of an induction motor has advantage of a quick torque response, and has been applied in various industrial applications.

In the design of a speed control system of induction motor, PI controller has been widely used because its structure is very simple. However, it is difficult to obtain robust and stable speed control characteristics because the gain of the controller can not be adjust automatically when the load disturbance or parameter changed.

The motor used in a reciprocating unbalanced load experience abrupt load change by the movement of piston. So, its speed is fluctuated.

This study proposes a new adaptive control system with conventional vector controller for a reciprocating unbalanced load. The proposed system consists of a load torque observer and a feed-forward compensation using neural network to obtain robust speed control characteristics. The observer designed based on the Gopinath' theory. And the neural estimator is consists of two layers, and is used to provide a real-time adaptive estimation of motor dynamics. The LMS(Least Mean Square) algorithm which has widely been used is applied as the learning algorithm for this network, to minimize the difference between the actual speed and that predicted value by the neural estimator.

To verify the effectiveness of this algorithm, a computer simulation test was carried out on the basis of theoretical consideration.