数据驱动的系统级综合
Data-Driven System Level Synthesis
摘要：
我们建立数据驱动版本的系统级综合（SLS）参数化的可实现的闭环系统响应的线性定常系统在有限的地平线。最近的工作的启发，数据驱动的控制，利用工具从行为理论，我们表明，最优化问题的系统响应，可以构成只使用过去的系统轨迹库，没有明确确定一个系统模型。我们首先考虑无噪声轨迹的理想化设置，并显示传统和数据驱动的SLS之间的确切等价性。然后，我们表明，在由过程噪声驱动的系统的情况下，来自鲁棒SLS的工具可以用来表征噪声对闭环性能的影响，并进一步利用来自矩阵集中的工具来表明，可以使用简单的轨迹平均技术来减轻这些影响。最后，我们的数值实验表明我们的方法的合理性。
英文摘要：
We establish data-driven versions of the System Level Synthesis (SLS) parameterization of achievable closed-loop system responses for a linear-time-invariant system over a finite-horizon. Inspired by recent work in data-driven control that leverages tools from behavioral theory, we show that optimization problems over system-responses can be posed using only libraries of past system trajectories, without explicitly identifying a system model. We first consider the idealized setting of noise free trajectories, and show an exact equivalence between traditional and data-driven SLS. We then show that in the case of a system driven by process noise, tools from robust SLS can be used to characterize the effects of noise on closed-loop performance, and further draw on tools from matrix concentration to show that a simple trajectory averaging technique can be used to mitigate these effects. We end with numerical experiments showing the soundness of our methods.
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