the research on linear motor control technology can be basically divided into three aspects: one is traditional control technology, the other is modern control technology, and the third is intelligent control technology. Traditional control technologies such as PID feedback control and decoupling control have been widely used in AC servo system. Among them, PID control contains information in the process of dynamic control, which has strong robustness and is the most basic control mode in AC servo motor drive system. In order to improve the control effect, decoupling control and vector control technology are often used. Under the condition that the object model is fixed, unchangeable and linear, and the operating conditions and operating environment are fixed, the traditional control technology is simple and effective. However, in the high-performance situation of high-precision micro-feed, the change of object structure and parameters must be considered. All kinds of time-varying and uncertain factors, such as nonlinear influence, change of operating environment and environmental interference, can get satisfactory control effect. Therefore, modern control technology has attracted great attention in the research of linear servo motor control. Common control methods are: adaptive control, sliding mode variable structure control, robust control and intelligent control. This paper mainly combines fuzzy logic, neural network with PID, H∞ control and other existing mature control methods to learn from each other's strong points to obtain better control performance.