Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Abstract

This study aims to design a control scheme that is capable to improve performance and efficiency of brushless DC motor (BLDC) in operating condition. The control scheme is composed of sliding mode controller (SMC) with proportionalintegral-derivative (PID) sliding surface. The PID sliding surface is used to improve the system transient response. Then, the SMC-PID is optimized by genetic algorithm optimization for further improvement on the stability and robustness against nonlinearities and disturbances. Chattering problem that appear in the SMC is minimized by employing an adaptive switching gain for the SMC that is integrated with Luenberger Observer. Lyapunov function candidate is applied to guarantee the stability of the system. Simulation on the proposed work is done in Matlab Simulink. Results of the simulation works indicate that the proposed control scheme can improve the transient response, the stability and robustness of the BLDC motor compared to the conventional SMC in the existence of nonlinearities and disturbances.

Hybrid Frequency and Period Based for Angular Speed Measurement of DC Motor Using Kalman Filter

“…Then, it will be compared with the state estimation, which results from the predicted step that results in error estimation, in Equation 6which is denoted as Y. 132 Y = ω p -H * ω (6) After estimating the error measurement, the Kalman parameter is updated using the Equation 7 below.…”

The Direct Torque Control of Brushless DC Motor Based on Sliding Mode Variable Structure

“…Ref. [10] uses a sliding mode change structure state observer to estimate the inversion momentum waveform and uses an extended Kalman filter to estimate the rotational speed, which realize the direct torque control of the brushless DC motor, and the stability and robustness of the direct torque control of the brushless DC motor are improved. Refs.…”