Proportional difference type iterative learning control algorithm based on parameter optimization

In order to enhance learning efficiency and obtain more accuracy transient tracking performances in iterative domain, a proposition difference type operator constructed “five terms” parameter optimal iterative learning control algorithm based on norm performance index is proposed. The convergence condition and necessary theoretic proof is given. And the reasons why the algorithm has better learning efficiency and monotone convergence performance are discussed in detail. Finally, a class of parameter optimal iterative learning control algorithm tracking performances with different structure is compared. Simulation show that the tracking error of the proposed algorithm in this paper converges monotonically and faster than other similar algorithms.