Application of fuzzy neural network in the speed control system of induction motor

Author(s):  
Li Yi ◽  
Pu Yonghong
2018 ◽  
Vol 42 (3) ◽  
pp. 286-297 ◽  
Author(s):  
Yong Li ◽  
Bohan Zhang ◽  
Xing Xu

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.


2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.


Sign in / Sign up

Export Citation Format

Share Document