Start-up current adaptive control for sensorless high-speed brushless DC motors based on inverse system method and internal mode controller

The start-up current control of the high-speed brushless DC (HS-BLDC) motor is a chal-lenging research topic. To effectively control the start-up current of the sensorless HS-BLDC motor, an adaptive control method is proposed based on the adaptive neural network (ANN) inverse system and the two degrees of freedom (2-DOF) internal model controller (IMC). The HS-BLDC motor is identified by the online least squares support vector machine (OLS-SVM) algo-rithm to regulate the ANN inverse controller parameters in real time. A pseudo linear system is developed by introducing the constructed real-time inverse system into the original HS-BLDC motor system. Based on the characteristics of the pseudo linear system, an extra closed-loop feed-back control strategy based on the 2-DOF IMC is proposed to improve the transient response per-formance and enhance the stability of the control system. The simulation and experimental results show that the proposed control method is effective and perfect start-up current tracking perfor-mance is achieved.