Performance Improvement of Direct Torque Control for Switched Reluctance Motor Using Neuro-Fuzzy Controller

Veena, P.; Jeyabharath, R.; Rajaram, M.

doi:10.2316/journal.201.2010.3.201-2118

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“…It can learn from the advantage of fuzzy logic and neural network to offset their weakness. But the fuzzy neural that adopts back-propagation algorithm is easy to get in the local minima [15].…”

Section: Introductionmentioning

confidence: 99%

Neural computation based vector controlled asynchronous motor fed by three levels NPC

2016

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This study presents an improved direct torque control based on artificial neural network techniques fed by a three levels neutral-point-clamped inverter for high power asynchronous motor drive. Indeed, the ANN is divided into four subnetworks, which are individually trained: flux estimation (supervised) with dynamic neurons, torque calculation (fixed-weight) with square neurons, flux angle encoder and magnitude calculation (supervised and fixed-weight) with "logsig" neurons and "tansig" neurons. The back-propagation learning rule is used to design the supervised neural network. The simple structure network facilitates a short training and processing times. The validity of the proposed approaches is confirmed by the simulation.Keywords Direct torque control · Three levels inverter · Induction motor · Artificial neural network List of symbolsR s , R r Stator and rotor resistance ( ) i sd, i sq Stator current dq axis (A) v sd, v sq Stator voltage dq axis (V) L s , L r , L m Stator, rotor, and mutual self inductance (H) ψ sd , ψ sq dq Stator flux (Wb) ψ rd , ψ rq dq Rotor flux (Wb) B Moulay Rachid Douiri

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Section: Introductionmentioning

confidence: 99%