A Novel Model Predictive Control for Five‐Phase Fault‐Tolerant Permanent Magnet Motor Using an Improved Vector Selection

ABSTRACT This paper proposes a novel model predictive control (MPC) for five‐phase fault‐tolerant permanent magnet motor (FTPMM) using an improved vector selection. Unlike conventional MPC approaches, this method does not rely on the deadbeat control principle. It effectively reduces the computational burden of the algorithm while enhancing the motor drive performance. The scheme determines the optimal output vector and calculates the corresponding action times based on the concept of synthesizing two adjacent virtual vectors (VVs). Firstly, a VVs control set is adopted to improve the current control accuracy. Then, an improved vector selection method is introduced to obtain the reference vector directly by table lookup method without computational derivation. This eliminates the dependence on the deadbeat control principle found in traditional methods. Then, a two‐step optimization method is used to obtain the optimal vector combination and to calculate the duty cycle. In the two‐step optimization process, constraints are introduced to avoid duty cycle overflow. This optimization process is also independent of the deadbeat control principle. The feasibility and superiority of the proposed MPC method are demonstrated through theoretical analysis and experimental validation.