An Ensemble Regulation Principle for Multiobjective Finite-Control-Set Model-Predictive Control of Induction Machine Drives

Finite-control-set model-predictive control (FCS-MPC) has been widely investigated in the electrical drive systems, thanks to its merits of intuitive concept, straightforward implementation, and fast transient response. Owing to the flexible inclusion of constraints, a combination of weighting parameters is derived in the objective function to balance the relationship between the control targets. However, it is a challenging and time-consuming task to optimize a series of weighting parameters. To cope with this issue, this article proposes an FCS-MPC scheme with an ensemble regulation principle for the removal of all the weighting parameters. On the basis of the dimension reduction of the optimization problem, the ensemble regulation principle initially selects the suboptimal solutions for all the control targets. The optimal solution is determined according to a high consistency with the suboptimal solutions via an adaptive mechanism, which not only achieves a decent performance but also avoids a worst case for all the control criteria. The experimental implementation is conducted on a 2.2-kW induction machine platform, which verifies that the proposed scheme outperforms a group of existing weighting factorless FCS-MPC schemes at both the steady state and the transient state.