Parameter-Free Ultralocal Model-Based Deadbeat Predictive Current Control for PMVMs Using Finite-Time Gradient Method

Traditional ultra-local model based predictive current control (UL-PCC) method has strong robustness on current control, however, the UL-PCC still rely on inductance to design the controller gain which really deteriorate its robustness on parameter. To solve it, a parameter free ultra-local model based deadbeat predictive current control (PF-DPCC) method using finite-time gradient method (FGM) is proposed in this paper for permanent magnet vernier motor (PMVM) drives. The UL-PCC is firstly modified considering rotor speed without any extra parameter. Then, the impact of initial controller gain on robustness is analyzed and an extreme low duty-cycle current signal which has negligible impact on current is injected to estimate the controller gain of PF-DPCC adaptively on the basis of the deadbeat concept and the FGM. Hence, all motor parameters are not required in advance and the initial controller gain can be set as 1 directly. Then, the robustness can be effectively improved and the dependence on initial value of controller gain can be eliminated. Finally, the effectiveness and the correctness of the proposed PF-DPCC are experimentally verified on a 400 W PMVM drive platform.