Trajectory Tracking between Josephson Junction and Classical Chaotic System via Iterative Learning Control

Abstract

This article addresses trajectory tracking between two non-identical systems with chaotic properties. To study trajectory tracking, we used the Rossler chaotic and resistive-capacitive-inductance shunted Josephson junction (RCLs-JJ) model in a similar phase space. In order to achieve goal tracking, two stages were required to approximate target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-JJ system into a quasi-Rossler system. Next, the RCLs-JJ system employs the proposed iterative learning control scheme in which the control signals are from the drive system to trace the trajectory of the Rossler system. The numerical results demonstrate the validity of the proposed method and the tracking system is asymptotically stable.