A method of incorporating experience in iterative learning controllers is proposed in this paper. It is proposed that if the previous experience of the controller can be used in the selection of the initial control input for a new desired trajectory tracking task, the convergence of the iterative learning controller can be improved without modifying the structure of the controllers. Therefore t...

In this paper, the conjugate direction method is used in designing a type of iterative learning control scheme for a kind of linear discrete time-invariant single-inputsingle-output systems, which is termed as a conjugate direction method based iterative learning control scheme. The convergence of the proposed learning scheme is analyzed by the technique of inductive inference in considering th...

In experimental control of quantum systems, the precision is often hindered by imperfect applied electronics that distort pulses delivered to target devices. To mitigate such error, deconvolution method commonly used for compensating distortion via a convolutional model. However, its effectiveness limited model inaccuracies (e.g., imprecise parameters or unmodeled dynamics). this article, we pr...

Iterative learning control (ILC) is a design method that can improve the tracking performance for systems working in repetitive manner by from previous iterations. Norm optimal ILC well known with appealing convergence properties, e.g. monotonic error norm convergence. However, it requires an explicit system model design, which be difficult or expensive to obtain practice. To address this probl...