scholarly journals Robust Adaptive Neural Sliding Mode Approach for Tracking Control of a MEMS Triaxial Gyroscope

10.5772/50915 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 20 ◽  
Author(s):  
Juntao Fei ◽  
Hongfei Ding ◽  
Shixi Hou ◽  
Shitao Wang ◽  
Mingyuan Xin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Tracking Control ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Adaptive Tracking Control ◽  
Mode Control ◽  
The Neural Network ◽  
Robust Adaptive ◽  
The Stability

In this paper, a neural network adaptive sliding mode control is proposed for an MEMS triaxial gyroscope with unknown system nonlinearities. An input-output linearization technique is incorporated into the neural adaptive tracking control to cancel the nonlinearities, and the neural network whose parameters are updated from the Lyapunov approach is used to perform the linearization control law. The sliding mode control is utilized to compensate the neural network's approximation errors. The stability of the closed-loop system can be guaranteed with the proposed adaptive neural sliding mode control. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode 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 Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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