Computationally Efficient Fixed Switching Frequency Direct Model Predictive Control

This paper presents a direct model predictive control (MPC) method for drive systems with superior steady-state and dynamic performance. Specifically, the discussed MPC algorithm achieves a steady-state behavior that is similar or better than that of a linear controller with a dedicated modulator, and fast transient responses that characterize direct controllers. Moreover, it ensures a fixed switching frequency by allowing for one switching transition per phase and sampling interval. Furthermore, the controller utilizes the stator current gradient to predict the evolution of the drive system within the prediction horizon. To find the optimal switching time instants-and thus ensure favorable performance-the control and modulation problems are formulated in one computational stage as a constrained quadratic program (QP). To solve the latter within a few microseconds, a computationally efficient QP solver based on a gradient method is proposed that enables the real-time implementation of the presented algorithm. To further alleviate the computational demands of the proposed method, a mechanism that can identify suboptimal switching sequences at the very early stages of the optimization process is proposed. The effectiveness of the proposed control scheme is experimentally verified on a 3 kW drive system consisting of a two-level inverter and an induction machine.