Optimizing the switching angles of SRM using adaptive neuro-fuzzy controller

2007 ◽  
Author(s):  
M.M. Beno ◽  
N.S. Marimuthu ◽  
N.A. Singh
Keyword(s):  
Fuzzy Controller ◽  
Neuro Fuzzy

A Neuro-Fuzzy Controller for Doubly FED Asynchronous Motor Drive

2010 ◽  
Vol 4 (1) ◽  
pp. 8-15
Author(s):  
Azeddine Chaiba ◽  
◽  
Rachid Abdessemed ◽  
M. Lokmen Bendaas ◽  
◽  
...  
Keyword(s):  
Fuzzy Controller ◽  
Asynchronous Motor ◽  
Motor Drive ◽  
Neuro Fuzzy ◽  
Doubly Fed

scholarly journals An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Jozef Živčák ◽  
Michal Kelemen ◽  
Ivan Virgala ◽  
Peter Marcinko ◽  
Peter Tuleja ◽  
...  
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mechanical Design ◽  
Mechanical Ventilator ◽  
Mechanical Ventilators ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

Neuro Fuzzy Controller for DTC of Induction Motor Using Multilevel Inverter with SVM

2021 ◽  
pp. 2150250
Author(s):  
B. Kiran Kumar ◽  
Y. V. Siva Reddy ◽  
M. Vijaya Kumar
Keyword(s):  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Reference Voltage ◽  
Training Dataset ◽  
Testing Time ◽  
Hybrid Technique ◽  
Nonlinear Loads ◽  
Neuro Fuzzy ◽  
Voltage Signal

In this paper, an effective neuro-fuzzy controller (NFC) technique has been proposed to control the induction motor torque and flux. The NFC hybrid technique is the grouping of the neural network (NN) and fuzzy logic controller (FLC), which generated the target voltages with the corresponding input flux and torque. The novelty of the proposed hybrid technique is highly flexible in nonlinear loads, convenient user interface and logical intellectual and permitting for integrated controlling schemes. Here, the FLC generates the training dataset of the NN technique based on the logical rules. The generated dataset contains the information about the flux and torque deviation parameters and the corresponding reference voltage parameters. The NN has been trained based on training dataset and the testing time which produces the optimal reference voltage parameters depends on the variation of the torque and flux parameters. By using the output of the NFC technique, the space vector modulation (SVM) develops the appropriate control pulses to the five-level inverter and the inverter generates the output voltage signal to the induction motor. The proposed method is designed in the MATLAB/Simulink platform and the outputs are verified through the comparison analysis with the existing techniques.

scholarly journals Improvement the Performance of Drum Boiler in thermal power plant Using Hybrid model through the Adaptive Neuro � fuzzy controller

2018 ◽  
Vol 11 (22) ◽  
pp. 1-7
Author(s):  
Khalid Faisal Sultan ◽  
Hosham Salim Anead ◽  
Malak Moneeryounis ◽  
◽  
◽  
...  
Keyword(s):  
Power Plant ◽  
Hybrid Model ◽  
Thermal Power Plant ◽  
Fuzzy Controller ◽  
Thermal Power ◽  
Drum Boiler ◽  
Neuro Fuzzy
Sign in / Sign up

Export Citation Format

Share Document