Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

Abstract

The direct torque control of the dual star induction motor (DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control (SOSMC) based on the super twisting algorithm (STA) combined with the fuzzy logic control (FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm (GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.

Keywords

double star induction machine / direct torque control / fuzzy second order sliding mode control / genetic algorithm / biogeography based optimization algorithm

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