A Fast Parameter Optimal Iterative Learning Control Algorithm

In order to obtain faster and more accuracy transient tracking performances in iterative domain, a high-order proportional integral difference type parameter optimal iterative learning control algorithm based on norm performance index is proposed. In the algorithm, the proportional integral difference type operator is introduced to expend the dimension of the algorithm and to increase the free-degree of the optimal parameter. Theoretic proof shows that the convergence of the algorithm is monotonic no matter the plant is positive or not, and the tracking error will converge monotonically to zero when the plant is positive. Finally, simulations show that the tracking error of the proposed algorithm converges monotonically and faster than other similar algorithms.