Long-Horizon Robust Direct Model Predictive Control for Medium-Voltage Induction Motor Drives With Reduced Computational Complexity

This paper proposes a long-horizon direct model predictive control (MPC) with reference tracking for medium-voltage (MV) drives that achieves favorable steady-state and transient behavior. However, as MPC is a model-based method, it is susceptible to parameter mismatches and variations of the machine. Moreover, even though a long prediction horizon significantly improves the steady-state behavior of the drive, it significantly increases the computational complexity of the direct MPC problem, rendering its real-time implementation a challenging—if not impossible—task. Motivated by these shortcomings of long-horizon direct MPC, this paper also aims to address them by enhancing the robustness of the developed control strategy, while keeping its computational complexity modest. To achieve the former, a prediction model suitable for MV drive systems is adopted that facilitates the effective estimation of the total leakage inductance of the machine. For the latter, the objective function of the MPC problem is formulated such that, even though the drive behavior is computed over a long prediction interval, only a few changes in the candidate switch positions are considered. The effectiveness of the proposed modeling, control, and estimation approaches is validated with hardware-in-the-loop (HIL) tests for an MV drive consisting of a three-level neutral point clamped (NPC) inverter and an induction machine (IM).