scholarly journals Identification of robust controller for 3hp 3Φ induction motor

2019 ◽  
Vol 8 (2) ◽  
pp. 125
Author(s):  
H. Sathishkumar ◽  
S. S. Parthasarathy
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Fuzzy Controller ◽  
Robust Controller ◽  
Cable Industry ◽  
Speed Controller ◽  
Neural Network Controller ◽  
Linear Load ◽  
Neuro Fuzzy ◽  
Non Linear Load

This paper deals about the identification of robust controller for 3hp 3Φ induction motor which is used in cable industry (Ravicab cables private limited) at Bidadi. In this cable industry 3hp 3Φ induction motor is used for cable pulling purpose. This industry is using PID (Proportional derivative integral) controller based VFD (Voltage frequency drive) for controlling the speed of this 3hp 3Φ induction motor. This VFD is not functioning well for the non linear load and disturbance environment. Therefore in this paper Neural network based speed controller is proposed as proposed controller-I for replacing the PID based VFD. Performance of the 3hp 3Φ induction motor is estimated when Neural network controller is interfaced with the motor. Then Neuro-fuzzy controller based speed controller is proposed as proposed controller-II for replacing the PID based VFD. Performance of the 3hp 3Φ induction motor is estimated when Neuro-fuzzy controller is interfaced with the motor. At last robust controller for the 3hp 3Φ induction motor which is used for cable pulling purpose is going to be identified by doing comparison chart between Neural network and Neuro-fuzzy controller.

Engineering Calculation And Algorithm Of Adaptation Of Parameters Of A Neuro-Fuzzy Controller

2021 ◽  
Vol 03 (09) ◽  
pp. 41-49
Author(s):  
I.H. Siddikov ◽  
◽  
P.I. Kalandarov ◽  
D.B., Yadgarova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Transient Time ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Neuro Fuzzy ◽  
Control Scheme

As part of the study, a control scheme with the adaptation of the coefficients of the neuron-fuzzy regulator implemented. The area difference method used as a training method for the network. It improved by adding a rule base, which allows choosing the optimal learning rate for individual neurons of the neural network. The neural network controller applied as a superstructure of the PID controller in the process control scheme. The dynamic object can function in different modes. This technological process operates in different modes in terms of loading and temperature setpoints. Because of experiments, the power consumption and the amount of time required maintaining the same absorption process, using a conventional PID controller and a neural-network controller evaluated. It concluded that the neuro-fuzzy controller with a superstructure reduced the transient time by 19%.

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 On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Hongwei Li ◽  
Kaide Ren ◽  
Haiying Dong ◽  
Shuaibing Li
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Fuzzy Controller ◽  
Synchronous Generator ◽  
Variable Universe ◽  
Neural Network Controller ◽  
Front End ◽  
Network Controller ◽  
Drive Chain

The rapid development of wind generation technology has boosted types of the new topology wind turbines. Among the recently invented new wind turbines, the front-end speed regulated (FSR) wind turbine has attracted a lot of attention. Unlike conventional wind turbine, the speed regulation of the FSR machines is realized by adjusting the guide vane angle of a hydraulic torque converter, which is converterless and much more grid-friendly as the electrically excited synchronous generator (EESG) is also adopted. Therefore, the drive chain control of the wind turbine owns the top priority. To ensure that the FSR wind turbine performs as a general synchronous generator, this paper firstly modeled the drive chain and then proposed to use the variable-universe fuzzy approach for the drive chain control. It helps the wind generator operate in a synchronous speed and outperform other types of wind turbines. The multipopulation genetic algorithm (MPGA) is adopted to intelligently optimize the parameters of the expansion factor of the designed variable-universe fuzzy controller (VUFC). The optimized VUFC is applied to the speed control of the drive chain of the FSR wind turbine, which effectively solves the contradiction between the low precision of the fuzzy controller and the number of rules in the fuzzy control and the control accuracy. Finally, the main shaft speed of the FSR wind turbine can reach a steady-state value around 1500 rpm. The response time of the results derived using VUFC, compared with that derived from a neural network controller, is only less than 0.5 second and there is no overshoot. The case study with the real machine parameter verifies the effectiveness of the proposal and results compared with conventional neural network controller, proving its outperformance.

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