Abstract: This paper addresses a kind of proportional-derivative-type (PD-type) iterative learning control updating law with feedback information for a class of linear time-invariant systems. By taking advantage of the generalized Young inequality of convolution integral, monotone convergence of the updating law is analyzed in the sense of Lebesgue-p norm. The analysis indicates that the convergence is determined not only by the system input-output matrix and the derivative learning gain, but also by the system state matrix and the proportional learning gain. It is further shown that the feedback information may speed up the convergence of the typical PD-type rule when the feedback gains are properly chosen. Numerical simulation exhibits the validity and the effectiveness.