Tolerant Sequential Model Predictive Direct Torque Control of Permanent Magnet Synchronous Machine Drives

Abstract

Due to the need to control simultaneously torque and flux in a permanent magnet synchronous machine (PMSM), their predictive control is a multiobjective optimization problem (MOOP). To solve this problem, traditional predictive control uses the weighting factors to convert the MOOP into a single-object optimization problem. Based on the lexicographic method, a simple strategy without weighting factors called tolerant sequential model predictive control (TSMPC) is proposed. In the proposed method, the cost functions of torque and flux are, respectively, placed in two layers of the sequential structure. The voltage vectors satisfying the torque tolerance are selected as the candidates for the next layer. Then, the candidate that can minimize the flux cost function will be executed by the inverter. The advantages of the proposed TSMPC method are that it can avoid cumbersome weighting factor adjustment and directly set the tolerance value based on performance requirements. The feasibility and stability of the TSMPC strategy are validated by theoretical analysis, and the experimental results show that it has good performance.