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.

中文翻译：

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不同粒度模型的组合鲁棒和随机模型预测控制

模型预测控制 (MPC) 中的长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，已经提出了一种利用不同粒度模型的鲁棒模型预测控制（RMPC）方法。控制范围内的预测分为使用 MPC 的详细模型的短期预测和使用 RMPC 的粗略模型的长期预测。在许多应用中，短期未来需要稳健性，但在长期未来，由于存在重大不确定性和潜在建模困难，稳健规划可能导致高度保守的解决方案。因此，我们建议将 RMPC 与短期预测的详细模型和随机 MPC (SMPC) 结合起来，在长期预测的简化模型上结合机会约束。由于长期预测的简单模型和不太保守的解决方案，这会减少计算工作量，因为只有短期预测才需要稳健性。该方法的有效性在移动机器人防撞仿真中得到了证明。