论文标题
通过区域反馈法律减少Min-Max模型预测控制的计算工作
Reducing the computational effort of min-max model predictive control with regional feedback laws
论文作者
论文摘要
最近，已经提出了一种区域MPC方法，该方法利用了最佳解决方案的分段仿射结构（以前无需计算整个显式解决方案）。在这里，区域是指使用当前操作状态附近最佳反馈定律的想法，因此提供了最佳输入信号，而无需求解QP。在本文中，我们将区域MPC的想法应用于最小MAX MPC问题。我们表明，新的强大方法可以显着减少在最小MAX MPC中解决的QP数量，从而减少总体计算工作。此外，我们使用具有不同视野的数值示例将新方法的性能与现有鲁棒区域MPC方法进行比较。最后，我们提供了一个基于视野的合适鲁棒区域MPC方法的规则。
Recently, a regional MPC approach has been proposed that exploits the piecewise affine structure of the optimal solution (without computing the entire explicit solution before). Here, regional refers to the idea of using the affine feedback law that is optimal in a vicinity of the current state of operation, and therefore provides the optimal input signal without requiring to solve a QP. In the present paper, we apply the idea of regional MPC to min-max MPC problems. We show that the new robust approach can significantly reduce the number of QPs to be solved within min-max MPC resulting in a reduced overall computational effort. Moreover, we compare the performance of the new approach to an existing robust regional MPC approach using a numerical example with varying horizon. Finally, we provide a rule for choosing a suitable robust regional MPC approach based on the horizon.
