本論文旨在藉由速度估測器來提高馬達速度控制的性能。在伺服馬達上，通常藉由編碼器感測位置，再由簡單的純微分器演算法將相臨兩次取樣間的位移量除以取樣時間以得到速度供回授控制之用，但這會導致量測到的雜訊被放大，因此必須依靠速度估測器來改善此問題。本論文研究了PI Servo-loop速度估測器、Levant微分器和卡門濾波器三種速度估測器，並且先以MATLAB/Simulink軟體進行模擬，接著將三種速度估測器都轉為差分方程式，再用MATLAB軟體模擬比較當以實際馬達位置訊號當輸入時，三種速度估測器速度估測之性能，確認實作之可行性。而在實作上，本系統使用德州儀器公司(Texas Instruments, TI)所生產的數位訊號處理器TMS320F28335做為核心以實現速度估測器演算法，經比較結果後以卡門濾波器在速度控制上的性能最佳。

SUMMARY
The aim of this thesis is to study the performance improvement of motor velocity control using velocity estimators. In practice, most servomotors use the encoder to measure the position of the motor and then use the simple differential algorithm, dividing the displacement between two sampling points by the sampling time, to obtain the velocity for feedback control. However, this way can result in serious noise amplification. In this study, velocity estimators are used to solve this problem. This thesis compares three velocity estimators including PI Servo-loop velocity estimator, Levant differentiator, and Kalman filter. First, MATLAB/Simulink are used to simulate these velocity estimation algorithms. For further validation, these velocity estimation algorithms are realized and tested in the difference formulas with the actual motor position signals. In experiments, the algorithms are implemented on a digital signal processor (TMS320F28335) from Texas Instruments. As a result, the Kalman filter outperforms the other velocity estimators in velocity control.

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