Research on the application of feedforward + high-order iterative learning of permanent magnet linear motor in wire bonding machine

Permanent magnet linear motors are widely used on XY motion platforms in semiconductor packaging equipment. This module has high requirements for motion accuracy. In order to solve the requirements for higher speed and accuracy of the XY motion platform in semi-conductive packaging equipment, a composite control algorithm of feedforward + high-order iterative learning is proposed, and a speed plan based on S-curve is designed for the working characteristics of the linear motor in the wire bonding machine. In this paper, the mathematical model of linear motor is established, and the convergence conditions of the high-order iterative learning control algorithm are given. Through the establishment of MATLAB/Simulink simulation model, the system trajectory tracking and error under the control algorithm of first-order iterative learning, high-order iterative learning, feedforward + high-order iterative learning are compared and analyzed. The simulation results show that high-order iterative learning has faster convergence speed than low-order iterative learning; the control algorithm of feedforward + high-order iterative learning can effectively solve the problem of slow convergence speed in later iterations, and the number of iterations is less than that of a single iterative learning algorithm. And the accuracy is higher. It meets the requirements of semiconductor packaging equipment for the fast and high-precision performance of linear motors, which is of great significance for improving the production efficiency of semiconductor products and improving product quality.