Simulation and Implementation of a Sliding Mode Control for a Brushless DC Motor with RBFNN and Disturbance Observer

Abstract

In this paper, a Sliding Mode Control (SMC) was built to control a Brushless DC Motor (BLDC) with a Radial Basis Function Neural Network (RBFNN) for estimating the unknown and uncertain parameters. Otherwise, a disturbance observer was also designed to improve the robustness to external load with smaller control gain. And a logic rectifier for the equivalent of DC motor, it makes the commutation strategies easier for operating the BLDC in 4Q. To enhance the flexibility and integration in developing the algorithm for a motor control system, the real system was modeled in MATLAB Simulink and the speed control structure was built up so that it can be worked in both simulation and experiment. The system performance was verified in dynamic load condition by analyzing the transient specification, steady-state error for tracking response and by evaluating the speed reduction, recovery time for regulation response. The platform was implemented with the DSP F28379D LaunchPad.