Control of a Uniform Step Asymmetrical 9-Level Inverter Based on Artificial Neural Network Strategy

A neural implementation of a harmonic elimination strategy for the control auniform step asymmetrical 9-level inverter is proposed and described in this paper. AMulti-Layer Perceptrons (MLP) neural network is used to approximate the mappingbetween the modulation rate and the required switching angles. After learning, the neuralnetwork generates the appropriate switching angles for the inverter. This leads to a lowcomputational-cost neural controller which is therefore well suited for real-timeapplications. This neural approach is compared to the well-known Multi-Carrier Pulse-Width Modulation (MCPWM). Simulation results demonstrate the technical advantages ofthe neural implementation of the harmonic elimination strategy over the conventionalmethod for the control of an uniform step asymmetrical 9-level inverter. The approach isused to supply an asynchronous machine and results show that the neural method ensures ahighest quality torque by efficiently canceling the harmonics generated by the inverter.