Terminal Sliding Mode Control for STT Missile Based on RBF Neural Network

2012 ◽  
pp. 49-54 ◽  
Author(s):  
Xianjun Shi ◽  
Hongchao Zhao ◽  
Jie Chen
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Terminal Sliding Mode Control ◽  
Terminal Sliding Mode ◽  
Mode Control

Neural Network Sliding Mode Control of 5DOFs Robotic Manipulator

2014 ◽  
Vol 898 ◽  
pp. 514-520 ◽  
Author(s):  
Chang Kai Xu ◽  
Ming Li ◽  
Jian Yin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Robotic Manipulator ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
The Stability

In this paper, a neural network sliding mode controller for a kind of 5DOFs robotic manipulator is proposed. A radial basis function (RBF) neural network is used as an estimator to approximate uncertainties of the system. The learning algorithm of the neural network improves the performance of the system. A globle terminal sliding mode control (GTSMC) is designed to guarantee the stability and improve the dynamic performance of the robotic manipulator. Simulation results show that the proposed NNSMC strategy is effective to ensure the robustness and dynamic performance of the 5DOFs robotic manipulator.

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