Adaptive Modeling of Brushless DC Motor Drive System

The superiority of control performance depends on the accuracy of the mathematical model of the system. However, it is difficult to establish precise mathematical models for control systems in industrial electromechanical systems. Establishing a high-precision brushless DC motor drive system model can improve the performance of the motion control system. This paper employs the method of Kalman filter to estimate states that rectifies original sparse sampled states and the method of online recursive least square to discern the unknown parameters of a forth order dynamical system with respect to the brushless DC motor driven systems. The model can be derived adaptively for the dynamical systems in real time. Experimental results show that the derived model agrees well with the actual output of the dynamical system. So, the proposed method provides a promising approach to the class of brushless DC motor driven systems with sparse feedback samples.