基于改进经验模态分解的直线电机伺服系统迭代学习控制OA

In order to address the issue of low convergence speed and poor tracking performance caused by error accumulation effects in iterative learning control of linear motor servo systems,a method based on a modified empirical mode decomposition algorithm is proposed.Firstly,a self-adaptive iterative learning position controller is designed.Subsequently,an improved algorithm based on the extension of triangular extreme wave and complementary set empirical mode decomposition is proposed.This algorithm can decompose the tracking errors of each iteration,screen and eliminate the components that affect error convergence.Through simulation analysis and a comparison with traditional iterative learning control,the paper demonstrates that the proposed method exhibits faster convergence speed and can achieve high-precision tracking control of linear motors with fewer iterations.