Fuzzy neural network-based chaos synchronization for a class of fractional-order chaotic systems: an adaptive sliding mode control approach

2020 ◽  
Vol 100 (2) ◽  
pp. 1275-1287 ◽  
Author(s):  
RenMing Wang ◽  
YunNing Zhang ◽  
YangQuan Chen ◽  
Xi Chen ◽  
Lei Xi
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Chaos Synchronization ◽  
Fuzzy Neural Network ◽  
Chaotic Systems ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Control Approach ◽  
Fuzzy Neural

scholarly journals Parallel robot with fuzzy neural network sliding mode control

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880126 ◽  
Author(s):  
Jiangmin Xu ◽  
Qi Wang ◽  
Qing Lin
Keyword(s):  
Neural Network ◽  
Control Theory ◽  
Sliding Mode Control ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Adaptive Sliding Mode Control ◽  
Mode Control ◽  
Fuzzy Neural

With the advancement in research on parallel robots, control theory is increasingly applied in the field of robotics. Owing to its robustness, sliding mode variable structure control is extensively used in parallel robots. This article presents an adaptive sliding mode control method for nonlinear systems. A parallel robot control model with adaptive fuzzy sliding mode control was designed based on a fuzzy neural network control theory, and simulation results demonstrate its effectiveness of the method.

scholarly journals Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2124
Author(s):  
Yunmei Fang ◽  
Fang Chen ◽  
Juntao Fei
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Parameter Uncertainties ◽  
Control Approach ◽  
Switching Law ◽  
Lumped Parameter ◽  
Fuzzy Neural ◽  
The Stability

In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface is designed, and the fractional order terms can provide additional freedom and improve the control accuracy. Then, the upper bound of lumped nonlinearities is estimated online using a double feedback fuzzy neural network. Accordingly, the gain of switching law is replaced by the estimated value. Meanwhile, the parameters of the double feedback fuzzy network, including base widths, centers, output layer weights, inner gains, and outer gains, can be adjusted in real time in order to improve the stability and identification efficiency. Finally, the simulation results display the performance of the proposed approach in terms of convergence speed and track speed.

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