Sliding Mode Control for the Hes1 Biochemical Reaction System Using RBF Neural Networks

2018 ◽  
Author(s):  
Taobei Su ◽  
Hui Lv ◽  
Changjun Zhou ◽  
Qiang Zhang
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Reaction System ◽  
Biochemical Reaction ◽  
Rbf Neural Networks ◽  
Mode Control

Application of Adaptive RBF-SMC for Electro-Hydraulic Position Servo System

2012 ◽  
Vol 463-464 ◽  
pp. 1440-1444
Author(s):  
Wu Wang ◽  
Zheng Yin Zhao
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Servo System ◽  
Good Control ◽  
Parameters Estimation ◽  
Rbf Neural Networks ◽  
Mode Control ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Electro-hydraulic servo system was hard to control with traditional control strategy and RBF-SMC (Radial Basis Function neural networks-Sliding Mode Control) controller was designed for this system. The mathematical model of the electro-hydraulic servo system was analyzed and the neural sliding mode controller was designed, the control law of sliding mode control was based on linearization feedback techniques and estimate parameters with RBF neural network. The simulation shows RBF neural networks can learning the uncertainties and disturbance, RBF-SMC has good control performance of reduces chattering and parameters estimation.

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