scholarly journals MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

2021 ◽  
Author(s):  
Susitra Dhanarajalu
Keyword(s):  
Regression Analysis ◽  
Fuzzy Inference ◽  
Simulated Data ◽  
Switched Reluctance Motor ◽  
Simulation Software ◽  
Accurate Estimation ◽  
Computationally Efficient ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Non Linear

This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data.

scholarly journals Adaptive neuro-fuzzy controller of switched reluctance motor

2007 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Ahmed Tahour ◽  
Hamza Abid ◽  
Ghani Aissaoui
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Digital Simulation ◽  
Switched Reluctance Motor ◽  
Motor Speed ◽  
Inference System ◽  
Switched Reluctance ◽  
Neuro Fuzzy ◽  
Reluctance Motor

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

scholarly journals Performance Tests of a PI Speed Controller Applied in a Non-linear Model of a Switched Reluctance Motor 8/6

2021 ◽  
Vol 16 ◽  
pp. 508-518
Author(s):  
J. D. González-San Román ◽  
J. U. Liceaga-Castro ◽  
I. I. Siller-Alcalá ◽  
E. Campero-Littlewood
Keyword(s):  
Linear Model ◽  
Viscous Friction ◽  
Switched Reluctance Motor ◽  
Performance Tests ◽  
Switched Reluctance ◽  
Speed Controller ◽  
Reluctance Motor ◽  
Non Linear ◽  
Point Set ◽  
Inverter Circuit

This article presents the performance tests, by simulation, of a classic PI speed controller applied to a switched reluctance motor (SRM) 8/6. The motor is represented by a linearization of the simplified non-linear model at an operating point set at 2000rpm. The model includes Coulomb plus viscous friction nonlinearity and an ideal inverter circuit. The control system simulations that are carried out are divided into two types: regulation tests and tracking tests, all simulations are carried out in Matlab® / Simulink software.

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