Indirect Model Predictive Control for Asymmetrical Six-Phase Drives Based on Disturbance Observer

To enhance the transient response of asymmetrical six-phase drive (ASD) systems and mitigate model inaccuracies, this paper presents an indirect model predictive control (MPC) method leveraging a disturbance observer (DOB). Specifically, a Kalman filter-based DOB is utilized to estimate system state errors arising from parameter mismatches and unmodeled dynamics. Subsequently, a high-bandwidth MPC strategy is developed to compute the unconstrained modulating voltage. Building on this, an active-set method-based quadratic programming (QP) solver is employed to determine the optimal constrained modulating voltage. Finally, the proposed approach is experimentally validated and compared with the field-oriented control (FOC) and conventional MPC methods on an asymmetrical six-phase permanent magnet synchronous motor (PMSM).