Exploiting models of different granularity in robust predictive control
Exploiting models of different granularity in robust predictive control
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DOI：
10.1109/cdc.2016.7798680
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Tobias Bäthge;S. Lucia;R. Findeisen
中科院分区：
文献类型：
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作者：
Tobias Bäthge;S. Lucia;R. Findeisen
在预测控制中使用长期的详细模型在计算上是具有挑战性的。此外，始终存在的不确定性使得在长时间范围内使用这种复杂的详细模型受到质疑，因为由此产生的轨迹的可变性。我们提出了一个多阶段的计划，结合使用不同粒度的模型-使用详细的模型进行短期预测，同时执行长期预测与不太详细的模型。使用投影和不变性性质的不同模型的复杂性和它们之间的过渡，我们表明，该计划是递归可行的。在仿真研究中，我们展示了如何将两种不同复杂度的模型相结合，通过障碍物的景观引导移动的机器人。
The use of detailed models over long horizons in predictive control can be computationally challenging. Furthermore, always-present uncertainty renders the use of such sophisticated detailed models over long time horizons questionable due to the resulting variability of the trajectories. We propose a multi-stage scheme that combines the use of models of different granularity - using detailed models for short-term predictions, while performing long-term predictions with less detailed models. Using projection and invariance properties for the different model complexities and the transitions between them, we show that this scheme is recursively feasible. In a simulation study, we show how two models of different complexity can be combined for steering a mobile robot through a landscape with obstacles.