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.

scholarly journals Handling Model and Implementation Uncertainties via an Adaptive Discrete Sliding Mode Controller Design

2016 ◽  
Author(s):  
Mohammad Reza Amini ◽  
Mahdi Shahbakhti ◽  
Selina Pan ◽  
J. Karl Hedrick
Keyword(s):  
Adaptive Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Electronic Control Unit ◽  
Control Technique ◽  
Propagation Mechanism ◽  
Sliding Mode Controller ◽  
Model Uncertainties ◽  
Control Approach ◽  
The Impact

Analog-to-digital conversion (ADC) and uncertainties in modeling the plant dynamics are the main sources of imprecisions in the design cycle of model-based controllers. These implementation and model uncertainties should be addressed in the early stages of the controller design, otherwise they could lead to failure in the controller performance and consequently increase the time and cost required for completing the controller verification and validation (V&V) with more iterative loops. In this paper, a new control approach is developed based on a nonlinear discrete sliding mode controller (DSMC) formulation to mitigate the ADC imprecisions and model uncertainties. To this end, a DSMC design is developed against implementation imprecisions by incorporating the knowledge of ADC uncertainties on control inputs via an online uncertainty prediction and propagation mechanism. Next, a generic online adaptive law will be derived to compensate for the impact of an unknown parameter in the controller equations that is assumed to represent the model uncertainty. The final proposed controller is an integrated adaptive DSMC with robustness to implementation and model uncertainties that includes (i) an online ADC uncertainty mechanism, and (ii) an online adaptation law. The proposed adaptive control approach is evaluated on a nonlinear automotive engine control problem in real-time using a processor-in-the-loop (PIL) setup with an actual electronic control unit (ECU). The results reveal that the proposed adaptive control technique removes the uncertainty in the model fast, and significantly improves the robustness of the controllers to ADC imprecisions. This provides up to 60% improvement in the performance of the controller under implementation and model uncertainties compared to a baseline DSMC, in which there are no incorporated ADC imprecisions.

Sliding Mode Control of a 2DOF Planar Pneumatic Manipulator

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Michaël Van Damme ◽  
Bram Vanderborght ◽  
Ronald Van Ham ◽  
Björn Verrelst ◽  
Frank Daerden ◽  
...  
Keyword(s):  
Feedback Linearization ◽  
Controller Design ◽  
Sliding Mode ◽  
Artificial Muscle ◽  
System Model ◽  
Sliding Mode Controller ◽  
Pneumatic Artificial Muscle ◽  
Actuator Dynamics ◽  
Pressure Dynamics ◽  
Muscle Actuators

This paper presents a sliding mode controller for a 2DOF planar pneumatic manipulator actuated by pleated pneumatic artificial muscle actuators. It is argued that it is necessary to account for the pressure dynamics of muscles and valves. A relatively detailed system model that includes pressure dynamics is established. Since the model includes actuator dynamics, feedback linearization was necessary to design a sliding mode controller. The feedback linearization and subsequent controller design are presented in detail, and the controller’s performance is evaluated, both in simulation and experimentally. Chattering was found to be quite severe, so the introduction of significant boundary layers was required.

Tracking Control of Robot Manipulator Using Sliding Mode Controller With Performance Robustness

1999 ◽  
Vol 121 (1) ◽  
pp. 64-70 ◽  
Author(s):  
Chieh-Li Chen ◽  
Rui-Lin Xu
Keyword(s):  
Tracking Control ◽  
Feedback Linearization ◽  
Controller Design ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Design Procedure ◽  
Sliding Mode Controller ◽  
Systematic Design ◽  
And Performance ◽  
The Stability

The tracking control problem of robot manipulator is considered in this paper. A sliding mode controller design with global invariance is proposed using the concept of extended system and feedback linearization. The sliding surface is assigned such that the sliding mode motion will occur while the proposed control law is applied. This results in a system with global invariance. The stability and performance of the resulting system can be guaranteed by the proposed systematic design procedure.

scholarly journals Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Tat-Bao-Thien Nguyen ◽  
Teh-Lu Liao ◽  
Jun-Juh Yan
Keyword(s):  
Neural Networks ◽  
Permanent Magnet ◽  
Sliding Mode ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Fuzzy Neural Networks ◽  
Sliding Mode Controller ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller ◽  
Fuzzy Neural

In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM) drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

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