Adaptive neural network second-order sliding mode control of dual arm robots

2017 ◽  
Vol 15 (6) ◽  
pp. 2883-2891 ◽  
Author(s):  
Le Anh Tuan ◽  
Young Hoon Joo ◽  
Le Quoc Tien ◽  
Pham Xuan Duong
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Second Order ◽  
Adaptive Neural Network ◽  
Mode Control ◽  
Dual Arm ◽  
Second Order Sliding Mode

scholarly journals Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

2019 ◽  
Vol 30 (4) ◽  
pp. 512-521 ◽  
Author(s):  
Thai Van Nguyen ◽  
Nguyen Huu Thai ◽  
Hai Tuan Pham ◽  
Tuan Anh Phan ◽  
Linh Nguyen ◽  
...  
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Adaptive Neural Network ◽  
Control Approach ◽  
Mode Control ◽  
Backstepping Sliding Mode Control ◽  
Dual Arm

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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