数控机床位置伺服系统的无模型自适应迭代学习控制

数控机床位置伺服系统在加工过程中受负载、摩擦和电路系统响应特性等因素影响,很难精确建立其加工过程动力学模型。针对批量零件加工过程中的重复执行过程,设计了一种数据驱动的无模型自适应迭代学习控制方案。该方案借助沿迭代轴的动态线性化方法,将数控机床位置伺服系统加工动力学过程等价转化成一个虚拟的迭代数据模型,并根据设计的迭代学习控制律和参数估计律构建数控机床位置伺服系统的无模型自适应迭代学习控制方案。仿真结果表明:该迭代学习控制方案基于数控机床重复运行的特点,仅利用位置和电机电流信息,完成了对零件加工过程的改善,提高了加工精度。

Model-free Adaptive Iterative Learning Control of CNC Machine Tool Position Servo System

CNC position servo systems is affected by load, friction, circuit system response characteristics, and other factors in machining process, so it is difficult to establish its dynamic model accurately.A model free adaptive iterative learning control scheme based on data driving was designed for the repeated process of the batch parts processing process.By virtue of the dynamic linearization method for the iterative axis, the dynamic process of CNC machine tool position servo system was equivalently transformed into a virtual iterative data model. A model free adaptive iterative learning control scheme for CNC machine tool position servo system was established based on the designed iterative learning control law and parameter estimation law. The simulation results show that in the iterative learning control scheme, only position and motor current information are used to improve the machining process and machining accuracy of parts based on the repeated operation characteristics of CNC machine tools.