Adaptive MIMO Controller Design for Chaos Synchronization in Coupled Josephson Junctions via Fuzzy Neural Networks

Abstract

Abstract. In this paper, we have discussed the synchronization between coupled Josephson Junctions which experience dierent chaotic oscillations. Due to potential high-frequency applications, the shunted nonlinear resistivecapacitive-inductance junction (RCLSJ) model of Josephson junction was considered in this paper. In order to obtain the synchronization, an adaptive MIMO controller is developed to drive the states of the slave chaotic junction to follow the states of the master chaotic junction. The developed controller has two parts: the fuzzy neural controller and the sliding mode controller. The fuzzy neural controller employs a fuzzy neural network to simulate the behavior of the ideal feedback linearization controller, while the sliding mode controller is used to ensure the robustness of the controlled system and reduce the undesired eects of the estimate errors. In addition, the Lyapunov candidate function is also given for further stability analysis. The numerical simulations are carried out to verify the validity of the proposed control approach.

Simplified Control Structure of Fuzzy Logic and Kalman Filter for Induction Motor Drive

“…Fuzzy logic control (FLC) is chosen because it is able to incorporate experience, intuition and heuristics into the system instead of relying on system dynamics models [33], or simulate behavior of controller [34]. In order to reduce computation time, FLC employs reduced number of fuzzy inference rules in IM drive with DTC [35].…”