Nonlinear Model Predictive Control of BLDC Motor with State Estimation

摘要

Brushless DC (BLDC) motor is the first choice for lightweight electric vehicles because of its high torque, high power density, and compatible speed range. The vehicle environment is very dynamic, nonlinear, and noisy. It is challenging to design a BLDC motor control for high-performance operation. Therefore this paper presents Nonlinear Model Predictive Control (NMPC) with online state estimation techniques for speed and torque control. We investigate the performance of three estimation techniques integrated with an NMPC strategy. The estimation techniques include Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Nonlinear Moving Horizon Estimation (NMHE). The comparative study is performed where the state variables are estimated from noisy measurements of output variables. Results of the closed-loop NMPC performance with three estimation techniques are presented and analyzed with different performance indicators. The results show the integration of NMHE with NMPC provides better performance than other estimation techniques. However, the NMHE is computationally expensive as compared to EKF and UKF.