Robust Model Predictive Torque Control of Interior PMSM Drives With Kalman-Based Disturbance Observer
Zebin Yang, Chenghang Miao, Xiaodong Sun, Dong Guo
Abstract
Due to the external noise disturbance, measurement error will be inevitably occurred in the measurement stage of model predictive torque control (MPTC), resulting in the wrong selection of the optimal voltage space vectors (VSVs) in the optimization stage of MPTC, and even affect the stability of the whole control system. This article proposed an improved MPTC scheme for interior PMSM (IPMSM) drives. In the proposed MPTC, a Kalman anti-disturbance observer (KADO) based on the Kalman filter (KF) is designed for estimating the mechanical angular velocity, rotor position and load torque accurately, which can suppress the poor performance of the system caused by the external disturbances. Moreover, to alleviate the calculated complexity, a switching table composed of active prediction vectors is set to predict the appropriate VSVs. The traditional MPTC with disturbances and without disturbances are compared with the proposed MPTC in the simulations and experiences, which proves the effectiveness of the proposed MPTC in suppressing white noise disturbance.