论文标题
Willems的线性描述系统的基本引理及其用于数据驱动的输出反馈MPC
Willems' fundamental lemma for linear descriptor systems and its use for data-driven output-feedback MPC
论文作者
论文摘要
在本文中，我们研究了离散时间线性描述系统的数据驱动的预测控制。具体而言，我们给出了Willems的基本引理的量身定制的变体，该变体表明，对于描述系统，与无代数约束的线性时间不变的系统相比，通过Hankel矩阵进行的非参数建模所需的数据更少。此外，我们使用此描述提出了一个数据驱动的框架，以最佳控制和预测离散时间线性描述符系统。对于后者，我们提供了足够的稳定性条件，可以在以示例为例中说明发现之前，为恢复摩尔为控制。
In this paper we investigate data-driven predictive control of discrete-time linear descriptor systems. Specifically, we give a tailored variant of Willems' fundamental lemma, which shows that for descriptor systems the non-parametric modelling via a Hankel matrix requires less data compared to linear time-invariant systems without algebraic constraints. Moreover, we use this description to propose a data-driven framework for optimal control and predictive control of discrete-time linear descriptor systems. For the latter, we provide a sufficient stability condition for receding-horizon control before we illustrate our findings with an example.
