DSVM-Based Model-Free Predictive Current Control of an Induction Motor

Classical model-free predictive current control (MFPCC) is a robust technique for two-level inverter-fed induction-motor drive, with advantages that consist of simple concept, rapid response, implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits significant ripple. This article presents method to enhance performance using combination discrete-space vector modulation (DSVM). The employs an ultralocal model extended-state observer (ESO) does not consider motor parameters, therefore improving system’s reliability by eliminating parameter dependency. proposed integrates DSVM, which divides single sample period into N equal intervals generates virtual vectors reduce stator It achieves minimum cost-function value across entire operating range (IM) drive selecting optimal from limited set permissible voltage vectors. Using DSVM effectively reduces total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate effectiveness superiority suggested over Finite-Control-Set (FCS) MFPCC, only considers real in its computations.