A new adaptive control method for induction motor (IM) drive using IMFO-RBFNN strategy with random pulse width modulation

Abstract

This paper proposes an optimal control of Induction Motor (IM) drives using a new optimization technique. The optimization technique is the joined execution of both the Improved Moth flame Optimization (IMFO) algorithm and Radial Basis Function Neural Network (RBFNN). The main objective of the proposed strategy is to enhance the control performance of the IM while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque, and speed. Here, the IMFO technique is optimized the gain parameters of the PI controller based on the IM speed variation and generates the reference quadrature axis current. By using the RBFNN, the reference three-phase current for accurate control pulses of the voltage source inverter (VSI) is predicted. The RBFNN is trained by the input motor actual quadrature axis current and the reference quadrature axis current with the corresponding target reference three-phase current. Furthermore, the proposed method control signals are connected with random pulse width modulation (RPWM) scheme and appropriate pulses are generated and applied to the inverter. With the proposed strategy, the control pulses of VSI are optimized and the proposed system offers a reliable solution. The proposed methodology is implemented in MATLAB/Simulink working platform. The performance of the IM drive is assessed by utilizing the comparative analysis with the existing techniques. The result obtained using the proposed optimization strategy showed that; it can provide the optimal control of IM drive. Also, the proposed strategy is effective in minimize the acoustic noise, torque ripple, eliminate the oscillation period with less computation, and reduces the complexity of the algorithm.

Keywords

Induction motor (IM)IMFO RBFNN total harmonic distortion (THD)random pulse width modulation (RPWM)

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