An Ensemble Regulation Principle for Multi-Objective 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. Due to the flexible inclusion of constraints, a combination of weighting parameters are 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 ensemble regulation principle for the removal of all the weighting parameters. On the basis of 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 merely achieves a decent performance but also avoid a worst case for all the control criteria. The experimental implementation is conducted on a 2.2 kW induction machine (IM) platform, which verifies that the proposed scheme outperforms a group of existing weighting factor-less FCS-MPC schemes at both the steady state and transient state.     «

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. Due to the flexible inclusion of constraints, a combination of weighting parameters are 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...     »