Iterative Gradient Descent-Based Finite Control Set Predictive Current Control With Least-Squares Optimized Duty Cycles

Finite control set predictive current control (FCS-PCC) is widely recognized as a competitive control strategy in the field of electrical drives, due to its superiority of fast dynamic response and low switching frequency. However, FCS-PCC is penalized by its inherent drawback that the discrete nature of switching states leads to relatively high torque and current deviations. In this article, an iterative gradient descent (GD) method combined with least-squares (LS) optimized duty cycles is presented to improve the steady-state performance of FCS-PCC. Unlike the cost function optimization in the conventional FCS-PCC, the quadratic programming problem is solved from a geometric perspective, by obtaining the GD that minimizes the tracking deviation in the fastest manner. To synthesize the GD, the optimal stator current derivatives in the current and previous iteration are employed, and their duty cycles are determined by the LS method. The abovementioned procedures are iteratively repeated in the dichotomy-based periods. The experimental performance of the proposed GD-based FCS-PCC is verified at an 8-kHz sampling frequency, which is compared with that of conventional and dichotomy-based FCS-PCC (DFCS-PCC). It is validated that the proposed algorithm outperforms the conventional and DFCS-PCC at both the steady state and the transient state.