Multivariable decoupling control based on fuzzy-neural network ath-order inverse system in fermentation process

2008 ◽  
Author(s):  
Sun Yukun ◽  
Wang Bo ◽  
Ding Shen Ping
Keyword(s):  
Neural Network ◽  
Fermentation Process ◽  
Fuzzy Neural Network ◽  
Inverse System ◽  
Decoupling Control ◽  
Fuzzy Neural ◽  
Multivariable Decoupling

Decoupling control for wastewater treatment process based on recurrent fuzzy neural network

2018 ◽  
Vol 21 (3) ◽  
pp. 1270-1280 ◽  
Author(s):  
Jun‐Fei Qiao ◽  
Gai‐Tang Han ◽  
Hong‐Gui Han ◽  
Cui‐Li Yang ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Decoupling Control ◽  
Wastewater Treatment Process ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

Robust control for permanent magnet in-wheel motor in electric vehicles using adaptive fuzzy neural network with inverse system decoupling

2018 ◽  
Vol 42 (3) ◽  
pp. 286-297 ◽  
Author(s):  
Yong Li ◽  
Bohan Zhang ◽  
Xing Xu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet ◽  
Fuzzy Neural Network ◽  
Speed Control ◽  
Inverse System ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Speed Control System ◽  
Fuzzy Neural

To eliminate the chattering phenomenon and effectively enhance the robustness and dynamic response of the speed control system of a permanent magnet in-wheel motor (PMIWM), a novel decoupling approach is proposed. The speed control system of the PMIWM is analyzed and modeled. By introducing the inverse model into the original PMIWM system, a new decoupling pseudo-linear system is established. A control method based on adaptive fuzzy neural network (AFNN) is investigated to obtain an accurate speed trajectory. The inverse system control approach is introduced into the AFNN-based control system. The PMIWM speed is decoupled completely by the proposed adaptive fuzzy neural network inverse (AFNNI) method. Experiments are carried out on a hardware-in-the-loop (HIL) test bench. Compared with traditional PID control scheme, the proposed AFNNI control strategy can realize a better speed control performance and ensure the robust stability of the PMIWM, even though the motor may suffer from both sudden change in velocity and severe variation under different drive cycles.

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