Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 31 May 2018 / Approved: 1 June 2018 / Online: 1 June 2018 (06:02:52 CEST)

A peer-reviewed article of this Preprint also exists.

Cheng, C.-K.; Chao, P.-P. Trajectory Tracking between Josephson Junction and Classical Chaotic System via Iterative Learning Control. Appl. Sci. 2018, 8, 1285. Cheng, C.-K.; Chao, P.-P. Trajectory Tracking between Josephson Junction and Classical Chaotic System via Iterative Learning Control. Appl. Sci. 2018, 8, 1285.

Journal reference: Appl. Sci. 2018, 8, 1285
DOI: 10.3390/app8081285

Abstract

This article addresses the trajectory tracking between two non-identical systems with chaotic properties. We employ the Rossler chaotic and RCL-shunted Josephson junctions model in similar phase space to study trajectory tracking. In order to achieve the goal tracking, we afford two stages to approximate the target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-J system into the quasi-Rossler system. Then next, the RCLs-J system employs the proposed the iterative learning control scheme and the control signal from the drive system to trace the trajectory of Rossler system. The numerical results demonstrate the proposed method and the tracking system is asymptotically stable.

Subject Areas

trajectory; chaos; josephson junction; RCL-shunted; iterative learning control (ILC)

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