Research on Incremental Global Optimization Model Predictive Current Control for Dual Three-Phase Open-Winding Permanent-Magnet Motors

Dual three-phase open-winding permanent-magnet synchronous motor (PMSM) drives offer high DC-bus utilization and fault tolerance, but conventional model predictive current control (MPCC) for this topology suffers from heavy rolling optimization, large harmonic currents, and sensitivity to machine parameters. This paper proposes an incremental global optimization model predictive current control (IGOMPCC) method. First, an incremental prediction equation over adjacent control periods cancels the permanent-magnet flux term in the reference voltage solution, eliminating dependence on the flux-linkage parameter. Second, inverter 1 selects its optimal vector from the 12 outermost large voltage vectors, while inverter 2 locks the reference voltage sector by deadbeat incremental voltage prediction, restricting candidate vector groups to two, with duty cycles solved in closed form by the Lagrange multiplier method; single-cycle cost function evaluations drop from 144 to 3. Third, two adjacent active vectors and a zero vector are synthesized at the analytical duty cycle to fix the equivalent switching frequency, concentrating harmonics and suppressing z1-z2 sub-plane currents. Simulation results show that, versus traditional MPCC, the settling time is shortened by 34.1% for a speed step and 20% for a load step, the peak d-axis current fluctuation is reduced by 52.9%, and the q-axis current steady-state error is nearly eliminated.