Data-driven model-free adaptive precision control for linear servo system
数据驱动的无模型自适应直线伺服系统精密控制和实现

To meet the requirements on the high-precision tool-servo system in modern manufacturing industry, we propose a data-driven model-free adaptive control (MFAC) method to control the position of this system. The design of controller requires no structure information of the linear servo system, but makes use of the estimated and predicted pseudo-partial derivatives (PPD) of the dynamic linearization model, that are obtained online from the input-voltage and the output-position of the linear motor. Under the same conditions in real experiments, this method brings about a positiontracking error which is 0.4mm to 2.6mm smaller than that of the PID method, and 0.2mm to 0.5mm smaller than that of the neural networks (NN) method. The robustness against the load disturbance is also improved.