Development of Sliding Mode Control-Adaptive Neuro-Fuzzy Inference Strategy Algorithm for Three-Phase 15-Level MLI

Abstract

A comparative analysis was conducted in current research on the control strategies utilized in three-phase 15-level Cascaded H-Bridge Multilevel inverter (CHBMLI)-fed different RL loads with a three-phase induction motor, generally applied in industries. The analysis of 15-level CHBMLI, with Sliding Mode Control (SMC), the sliding mode control with an artificial neural network (SMC-ANN), and sliding mode control with an Adaptive Neuro-Fuzzy Inference Strategy (SMC-ANFIS) controller are explained. The study compared the performance achieved by controllers at four different speeds of a 3ϕ inductor motor and parallel-connected RL load dynamic change under closed-loop operations. The comparative study results demonstrate that there should be a reduction in the methodology suggested in terms of both the quantity of power devices and Total Harmonic Distortion (THD). The researcher conducted the experimentation procedures to authenticate the proposed 15-level MLI topology's performance. The SMC-ANFIS control technique produces less voltage and lower current total harmonic distortion of 5.53% and 2.74% compared to the SMC-ANN control technique in both simulation and real-time implementations.