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.

Estimasi Kecepatan Motor Brushless DC dengan Menggunakan Metode Sliding Mode Observer

“…Secara teoritis, torsi puncak terjadi ketika medan magnet stator dan rotor berada pada saat 90°. Kumparan harus diberi sumber tenaga agar motor tetap berjalan sehubungan dengan rangkaian tertentu dengan mempertimbangkan informasi posisi dan ini disebut sebagai komutasi (Putra, Has, & Effendy, 2018).…”

A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor

“…Conventional control methods cannot resist these alterations and lose their precision. Thus, it was necessary to implement advanced control techniques to solve this problem, especially those based on the artificial intelligence, such as: fuzzy control [32,33], neural control [34,35], Genetic Algorithm (GA) control [36,37], PSO control [38], BAT control [31] and recently, FA control and Improved Firefly Algorithm (IFA) or Modified Firefly Algorithm (MFA) [24][25][26][27][28]. These methods are based essentially on the optimization of the PID corrector parameters and its derivatives to obtain optimal performance.…”