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초록

In this paper, we propose a position-speed control of servo system with a state estimator. And also we utilized two mass modeling in order to deals with real system accurately. The overall control system consists of two parts : the position-speed controller and state estimator. The Kalman filter applied as state-feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear, unbiased and minimum error variance recursive algorithm to estimate the unknown state optimally. Therefore we consider the error problem about the servo system modeling, the measurement noise at low-speed ranges a stochastic system, and implement a optimal state observer. Performance of the proposed state estimator are demonstrated by computer simulations.