On-line Genetic Fuzzy-neural Sliding Mode Controller Design

2006 ◽  
Author(s):  
Ping-Zong Lin ◽  
Wei-Yen Wang ◽  
Tsu-Tian Lee ◽  
Guan-Ming Chen
Keyword(s):  
Controller Design ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Fuzzy Neural ◽  
On Line

Sliding Mode Control of Nonlinear Mechanical Vibrations1

2000 ◽  
Vol 122 (4) ◽  
pp. 674-678 ◽  
Author(s):  
Hebertt Sira-Ramı´rez ◽  
Orestes Llanes-Santiago
Keyword(s):  
Design Methodology ◽  
Controller Design ◽  
Sliding Mode ◽  
Mechanical Vibration ◽  
Differential Flatness ◽  
Sliding Mode Controller ◽  
Nonlinear Mechanical ◽  
Active Stabilization ◽  
On Line ◽  
Line Trajectory

In this article we illustrate how the property of differential flatness can be advantageously joined to the sliding mode controller design methodology for the active stabilization of nonlinear mechanical vibration systems. The proposed scheme suitably combines off-line trajectory planning and an on-line “smoothed” sliding mode feedback trajectory tracking scheme for regulating the evolution of the flat output variables toward the desired equilibria. [S0022-0434(00)00404-4]

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

2017 ◽  
Vol 1 (1) ◽  
pp. 80
Author(s):  
Thien Bao Tat Nguyen
Keyword(s):  
Josephson Junctions ◽  
Feedback Linearization ◽  
Controller Design ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Sliding Mode Controller ◽  
Neural Controller ◽  
Control Approach ◽  
Creative Commons ◽  
Fuzzy Neural

In this paper, we have discussed the synchronization between coupled Josephson Junctions which experience different chaotic oscillations. Due to potential high-frequency applications, the shunted nonlinear resistive-capacitive-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 effects 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. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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