Gradient Descent-Based Objective Function Reformulation for Finite Control Set Model Predictive Current Control With Extended Horizon

This article proposes a novel objective function formulation based on gradient descent (GD) for finite control set predictive current control (FCS-PCC) with extended horizon. FCS-PCC has become increasingly attractive for electrical drive applications owing to its short settling time, lower switching frequency, capability to handle multiple conflicting targets, and feasible inclusion of constraints. However, it still suffers from high-torque ripple and poor current quality at the steady state. To tackle the aforementioned issue, a GD-based objective function reformulation is employed in the FCS-PCC with extended horizon. First, the optimization problem underlying FCS-PCC is formulated as a constrained quadratic programming problem with proved convexity from a geometric perspective. Based on the above, the tracking error of the control objective is minimized more efficiently by searching along the direction of GD. Consequently, the objective function is reconstructed as the deviation between the normalized GD and derivative, combined with the extension of feasible set. The abovementioned procedures are iteratively learned in every prediction horizon. The effectiveness of the proposed algorithm is verified on a 2.2-kW induction machine platform with a prediction horizon of $N$ = 3. It is confirmed that the proposed algorithm outperforms the conventional and multistep FCS-PCC in steady state and transient state.