Chebyshev neural network observer based RBF neural network terminal sliding mode controller for a class of nonlinear system

2018 ◽  
Author(s):  
Hadjer Sioud ◽  
Amin Sharafin ◽  
Karam Eliker ◽  
Weidong Zhang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Sliding Mode Controller ◽  
Terminal Sliding Mode ◽  
Chebyshev Neural Network ◽  
Neural Network Observer

RBF Neural Network-Based Terminal Sliding Mode Control for Reentry Warhead

2011 ◽  
Vol 63-64 ◽  
pp. 381-384
Author(s):  
Hong Chao Zhao ◽  
Jie Chen ◽  
Hua Zhang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Finite Time ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Research Result ◽  
Sliding Mode Controller ◽  
Moving Mass ◽  
Terminal Sliding Mode ◽  
Moving Mass Control

The existing moving mass control system of a nonspinning reentry warhead could not drive the system error to reach zero in finite time. In order to settle the finite time reach issue, an RBF neural network-based terminal sliding mode controller was presented to design the moving mass control system. It used a terminal sliding mode to ensure that the error reaches zero in finite time. The disturbance and coupled terms of the warhead were treated as uncertainties. An RBF neural network was used to estimate the uncertainties. A nonspinning warhead was taken in the simulation to test the performance of the presented controller. The simulation results show the presented controller has faster tracking speed and higher tracking precision than the former research result.

Fuzzy Sliding Mode Control for a Three-Links Spatial Robot Based on RBF Neural Network

2011 ◽  
Vol 141 ◽  
pp. 303-307 ◽  
Author(s):  
Sheng Bin Hu ◽  
Min Xun Lu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Sliding Mode Controller ◽  
System A ◽  
Radial Basis ◽  
Fuzzy Sliding Mode

To achieve the tracing control of a three-links spatial robot, a adaptive fuzzy sliding mode controller based on radial basis function neural network is proposed in this paper. The exponential sliding mode controller is divided into two parts: equivalent part and exponential corrective part. To realize the control without the model information of the system, a radial basis function neural network is designed to estimate the equivalent part. To diminish the chattering, a fuzzy controller is designed to adjust the corrective part according to sliding surface. The simulation studies have been carried out to show the tracking performance of a three-links spatial robot. Simulation results show the validity of the control scheme.

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