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Appl. Sci. 2018, 8(8), 1285; https://doi.org/10.3390/app8081285

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

Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
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Received: 31 May 2018 / Revised: 24 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2018)
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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. View Full-Text
Keywords: trajectory; chaos; resistive–capacitive–inductance shunted Josephson Junction (RCLs-JJ); Iterative Learning Control (ILC) trajectory; chaos; resistive–capacitive–inductance shunted Josephson Junction (RCLs-JJ); Iterative Learning Control (ILC)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Cheng, C.-K.; Chao, P.C.-P. Trajectory Tracking between Josephson Junction and Classical Chaotic System via Iterative Learning Control. Appl. Sci. 2018, 8, 1285.

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