On Direct vs Indirect Data-Driven Predictive Control
On Direct vs Indirect Data-Driven Predictive Control
复制标题
关于直接与间接数据驱动的预测控制
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
F. Pasqualetti
中科院分区：
文献类型：
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作者：
Vishaal Krishnan;F. Pasqualetti
在这项工作中，我们比较了直接和间接的方法，随机线性时不变系统的数据驱动的预测控制。这两种方法之间的区别在于，间接方法涉及从数据中识别低维模型，然后将其用于确定性等效控制设计，而直接方法完全避免了这一中间步骤。工作在一个基于优化的框架，我们发现，次优差距衡量控制性能w.r.t.基于模型的最优控制设计仅在直接方法中随着数据集的大小而消失，而间接方法引起渐近偏差。另一方面，通过依赖于低维模型的识别，间接方法具有较低的方差，并且对于较小的数据集优于直接方法。最终，通过揭示直接和间接数据驱动的预测控制设计的性能的两个非渐近制度的存在，我们的研究表明，这两种方法都不总是上级和设计的选择，在实践中，必须通知可用的数据集。
In this work, we compare the direct and indirect approaches to data-driven predictive control of stochastic linear time-invariant systems. The distinction between the two approaches lies in the fact that the indirect approach involves identifying a lower dimensional model from data which is then used in a certainty-equivalent control design, while the direct approach avoids this intermediate step altogether. Working within an optimization-based framework, we find that the suboptimality gap measuring the control performance w.r.t. the optimal model-based control design vanishes with the size of the dataset only with the direct approach, while the indirect approach incurs an asymptotic bias. On the other hand, the indirect approach, by relying on the identification of a lower dimensional model, has lower variance and outperforms the direct approach for smaller datasets. Ultimately, by revealing the existence of two non-asymptotic regimes for the performance of direct and indirect data-driven predictive control designs, our study suggests that neither approach is invariably superior and that the choice of design must, in practice, be informed by the available dataset.
DOI：
10.1109/icra.2019.8794213
发表时间：
2018-09
期刊：
2019 International Conference on Robotics and Automation (ICRA)
影响因子：
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作者：
Vincent Pacelli;Anirudha Majumdar
通讯作者：
Vincent Pacelli;Anirudha Majumdar