PD iterative learning control based on neural network and genetic parameter optimization

In this paper, an optimal iterative PD learning control algorithm based on neural network and genetic optimization algorithm is proposed for improving traditional PD-type iterative learning algorithm. The best PD controller parameters can be obtained by this algorithm. The simulation results shows that the algorithm can improve the accuracy of the iterative control effectively, and the control performance of the algorithm has improved significantly compared to the traditional P-type or PD-type algorithms.