A Low-Complexity Gradient Descent Solution with Backtracking Iteration Approach for Finite Control Set Predictive Current Control

Artikel i vetenskaplig tidskrift, 2022

Finite control set model predictive control (FCS-MPC) has been widely recognized in the field of electrical drive control during the past decades, due to its merits of quick dynamic response and low switching frequency. However, it is inherently penalized by the high tracking deviations in the steady state as well as exhaustive search among the switching sequences. To cope with this issue, a low-complexity gradient descent based finite control set predictive current control (GD-FCSPCC) combined with backtracking optimized iteration approach is proposed in this paper, aiming to improve the control performance by effectively tracking the reference value. Firstly, FCS-PCC is reformulated as a quadratic programming (QP) problem from a geometric perspective. Consequently, the convexity of QP problem is proved to underlying the gradient descent to minimize the tracking error in an effective manner. Thus, the control objectives are determined by optimizing the deviation between the gradient descent and the stator current derivative in a cascade structure, to reduce the number of enumerated sequences. The procedures are repeated in the iteration periods optimized via a backtracking search method, until the stopping criterion is satisfied. The effectiveness of the proposed GD-FCSPCC is experimentally validated on a 2.2 kW induction machine testbench.