In this paper, a new algorithm for an adaptive proportional-Integrator (PI) controller for nonlinear systems subject to stochastic non-Gaussian disturbance is studied. The minimum entropy control is applied to decrease the closed-loop tracking error under an iterative learning control (ILC) basis. The key issue here is to divide the control horizon into a number of equal time intervals called batches. Within each intervals there are a fixed number of sample points. The design procedure is divided into two main algorithms, within each batch and between any two adjacent batches. However, within each batch the (PI) coefficients are fixed. A sufficient condition has been established to guarantee the stability of the closed-loop system. An illustrated example of two-link is included to demonstrate the use of control algorithm, and satisfactory resultshave been obtained.