论文标题
与不同粒度模型的稳健和随机模型预测控制
Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity
论文作者
论文摘要
模型预测控制（MPC）中的长期预测范围通常被证明是有效的，但是，这会增加计算成本。最近，已经提出了一种健壮的模型预测控制（RMPC）方法，该方法利用了不同粒度模型。使用MPC的详细模型和使用RMPC的粗制模型将对控制范围的预测分为短期预测。在许多应用中，短期未来需要鲁棒性，但是在长期的未来，要遵守重大的不确定性和潜在的建模困难，强大的计划可能会导致高度保守的解决方案。因此，我们建议在短期预测和随机MPC（SMPC）的详细模型上组合RMPC，并具有机会限制，并在简化的模型上进行长期预测。由于短期预测的简单模型，由于简单的模型而导致的计算工作减少，因为仅短期预测才需要鲁棒性。该方法的有效性显示在移动机器人碰撞避免模拟中。
Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models of different granularity. The prediction over the control horizon is split into short-term predictions with a detailed model using MPC and long-term predictions with a coarse model using RMPC. In many applications robustness is required for the short-term future, but in the long-term future, subject to major uncertainty and potential modeling difficulties, robust planning can lead to highly conservative solutions. We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions. This yields decreased computational effort due to a simple model for long-term predictions, and less conservative solutions, as robustness is only required for short-term predictions. The effectiveness of the method is shown in a mobile robot collision avoidance simulation.
