Iterative learning control with reference batch for linear time-variant systems

A new iterative learning control method is presented for the trajectory tracking control of linear time-variant systems. This new method does not need detailed knowledge of the controlled system. However, a reference batch is designed, in which some small change of the input trajectory in the current batch is applied to the controlled system, and then its output trajectory is obtained. The ratio of the input change and corresponding output change in the current and reference batch is used as the gain in the learning law for calculating the inputs of the next batch. Thus the method can track the desired trajectory successfully while the iteration is running on. The convergence of the proposed method is analyzed, and the method is validated on a typical linear time-variant system.