基于遗传算法迭代学习的偏心轴廓形误差补偿研究

为了提高偏心轴类零件轮廓加工精度,引入遗传算法和迭代学习PID控制算法,利用遗传算法对偏心轴磨床不同转速下的X-C轴PID参数进行整定,再通过迭代学习PID控制方法对X-C轴进行迭代学习控制,减小偏心轴磨床X-C轴的跟踪误差,通过MATLAB的Simulink仿真工具建立偏心轴磨削迭代学习PID控制仿真程序,进行仿真实验。实验表明基于遗传算法的PID迭代学习控制比普通PID控制更能够有效控制X-C轴跟踪误差,提高偏心轴轮廓加工精度。

Research on Offset Axis Profile Error Compensation Based on Iterative Learning of Genetic Algorithm

In order to improve the contour processing precision of eccentric shaft parts, the genetic algorithm and iterative learning PID control algorithm are introduced, and the X-C axis PID parameters of the eccentric shaft grinder at different speeds are studied by genetic algorithm. Through the iterative learning PID control method, iterative learning control is performed on the X-C axis, and the tracking error of the XC axis of the eccentric shaft grinding machine is reduced. The eccentric axis grinding grinding iterative learning PID control simulation program is established by MATLAB simulink simulation tool, and the simulation experiment is carried out. Experiments show that the PID iterative learning control based on genetic algorithm can effectively control the X-C axis tracking error and improve the eccentric axis machining accuracy than the ordinary PID control.