scholarly journals Retracted: Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-1
Author(s):  
Complexity
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Sliding Mode ◽  
Degree Of Freedom ◽  
Robotic Exoskeleton ◽  
Adaptive Sliding Mode ◽  
Model Free

scholarly journals Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangjian Chen ◽  
Di Li ◽  
Pingxin Wang ◽  
Xibei Yang ◽  
Hongmei Li
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Sliding Mode ◽  
Robotic Exoskeleton ◽  
Adaptive Sliding Mode ◽  
Model Free ◽  
Control Scheme ◽  
System Uncertainties ◽  
Adaptive Control Scheme

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

scholarly journals Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40076-40085
Author(s):  
Ngoc Phi Nguyen ◽  
Nguyen Xuan Mung ◽  
Ha Le Nhu Ngoc Thanh ◽  
Tuan Tu Huynh ◽  
Ngoc Tam Lam ◽  
...  
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Adaptive Sliding Mode ◽  
Mode Control

scholarly journals Improved Model-Free Adaptive Sliding-Mode-Constrained Control for Linear Induction Motor considering End Effects

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoqi Song ◽  
Dezhi Xu ◽  
Weilin Yang ◽  
Yan Xia ◽  
Bin Jiang
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Research Field ◽  
Constrained Control ◽  
End Effects ◽  
Linear Induction ◽  
Adaptive Sliding Mode ◽  
Model Free ◽  
Improved Model

As a kind of special motors, linear induction motors (LIM) have been an important research field for researchers. However, it gives a great velocity control challenge due to the complex nonlinearity, high coupling, and unique end effects. In this article, an improved model-free adaptive sliding-mode-constrained control method is proposed to deal with this problem dispensing with internal parameters of the LIM. Firstly, an improved compact form dynamic linearization (CFDL) technique is used to simplify the LIM plant. Besides, an antiwindup compensator is applied to handle the problem of the actuator under saturations in case during the controller design. Furthermore, the stability of the closed system is proved by Lyapunov stability method theoretically. Finally, simulation results are given to demonstrate that the proposed controller has excellent dynamic performance and stronger robustness compared with traditional PID controller.

scholarly journals Adaptive PD Sliding Mode Control For 4 DOF SCARA Variant

2021 ◽  
Author(s):  
Manjeet Tummalapalli
Keyword(s):  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
High Speed ◽  
Sliding Mode ◽  
Adaptive Controller ◽  
Degree Of Freedom ◽  
Tracking Performance ◽  
Advantages And Disadvantages ◽  
Model Free ◽  
Adaptive Law

This project proposes a new SCARA variant with 4 degree of freedom. The proposed variant is achieved by swapping joint 2 and joint 3 of the standard SCARA robots. An adaptive controller is defined based on the advantages and disadvantages of PD, and SMC controllers.The purpose of the project is to understand the dynamics of the variant and to track the performance for trajectories. Simulations for tracking performance are carried under linear and circular trajectories. The variant is studied over the three controllers; PD, PD-SMC and A-PD-SMC. The variant under the adaptive controller is most efficient in terms of tracking performance and the control inputs to the system. The system is simulated under high speed and with the influence of friction at the joints. The control gains are held constant for both the trajectories and hence the controller is able to perform good under changing trajectories. Due to the use of the adaptive law, the system is at the ease of implementation and since no priori knowledge if the system is needed, it is model free. Therefore, the proposed adaptive PD-SMC has proven to provide good, robust trajectory tracking.

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