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In this paper, a new application of the Selected Harmonic Elimination Pulse Width Modulation (SHEPWM) technique used in the cascade multilevel inverter topology which is formed by series connections of one‐phase bridge type inverters (H‐bridge) is introduced. The advantage of the SHEPWM technique is its ability to operate in low switching frequency that makes it suitable for high power applications.

First, the switching angles are calculated using constrained optimization technique. By using these switching angles, the fundamental harmonic can be controlled and the selected harmonics can be eliminated. Then, using these calculated switching angles, a set of equation is formed which calculate the switching angles with respect to the modulation index. The switching angles at any modulation index can be easily obtained by solving the equation set. In this study, this equation set has been solved online using dSPACE DS1103 controller board. Using this technique, three‐phase voltages have been obtained from a five‐level cascade inverter. These voltages are applied to an induction motor.

The simulation results are verified by the experimental results. The results show that selected harmonics can be eliminated and an ac voltage with variable amplitude and frequency can be obtained using the proposed technique.

This paper presents a new application of the (SHEPWM) technique for multilevel inverters.

The paper aims to propose an adaptive and robust on‐line trained neuro‐fuzzy current controller based on indirect field oriented control (IFOC) for the current control of multilevel inverter fed induction motor (IM).

Torque current of IM is controlled with Sugeno type neuro‐fuzzy controller (NFC) which has the ability of self tuning against parameter variations and load disturbance. Input variables of the neuro‐fuzzy current controller are chosen error and integral of error in order to eliminate steady state error. The consequent parameters of neuro‐fuzzy current controller are trained on‐line through backpropagation learning algorithm.

The validity of proposed current control algorithm is shown with experimental results carried out under different speed commands, parameter variations and load disturbances. The experimental results show that control performance of NFC in the current control of IMs is satisfactory because of its adaptive and robust structure.

This paper presents the design of an on‐line trained neuro‐fuzzy current control to improve the current control performance. The performance of the current controller largely depends on using converter systems. In this study, a multilevel inverter is used to obtain less harmonic distortion and near sinusoidal form of output voltage and current waveforms of the converter.