Direct Model Predictive Control: A Review of Strategies That Achieve Long Prediction Intervals for Power Electronics

摘要

Direct model predictive control (MPC) strategies that achieve long prediction horizons with a modest computational complexity are reviewed in this article, focusing on power electronics applications. In many MPC problems, a long prediction horizon is required to ensure an adequate closed-loop performance in steady state and to avoid stability issues. However, the computational effort of solving the optimization problem underlying MPC problems with long prediction horizons is often very great, making the implementation of such schemes in real time a difficult and challenging task. To overcome this difficulty, three established methodologies are surveyed that yield long prediction horizons with a modest computational burden. Case studies are investigated to substantiate the merits of these schemes. More specifically, for dc?dc boost converters, a move blocking strategy is reviewed, and for ac medium-voltage (MV) drives, both an extrapolation and an event-based horizon strategy are examined.