Abstract

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 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 a backtracking iteration approach is proposed in this article, aiming to improve the control performance by effectively tracking the reference value. First, 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, which minimizes the tracking deviations in an effective manner. Thus, the optimal solutions are selected by optimizing the reformulated objective functions. To reduce the number of the searched control inputs, a two-layer generalized decision-tree is employed. The procedures are repeated in several iteration periods optimized via a backtracking 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.