具有非均匀预测时域的不同粒度模型的模型预测控制
Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
摘要：
时域长度和模型精度是设计模型预测控制器时的决定性因素。虽然长视野和详细的模型对控制性能有积极的影响，但计算复杂性增加。随着预测在水平长度上变得不那么精确，值得研究不同模型和不同时间步长的组合。在这里，我们提出了一个模型预测控制计划，分为两个部分的预测范围。一个详细的模型用于短期预测范围和一个简化的模型，增加了采样时间的长期范围。这种方法结合了长预测范围的优点，由于简化的模型和更少的决策变量，减少了计算工作量。所提出的模型预测控制是递归可行的。仿真研究表明，所提出的方法的有效性：采用一个较长的预测范围的优势，计算复杂度。
英文摘要：
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control performance, computational complexity increases. As predictions become less precise over the horizon length, it is worth investigating a combination of different models and varying time step size. Here, we propose a Model Predictive Control scheme that splits the prediction horizon into two segments. A detailed model is used for the short-term prediction horizon and a simplified model with an increased sampling time is employed for the long-term horizon. This approach combines the advantage of a long prediction horizon with a reduction of computational effort due to a simplified model and less decision variables. The presented Model Predictive Control is recursively feasible. A simulation study demonstrates the effectiveness of the proposed method: employing a long prediction horizon with advantages regarding computational complexity.
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