论文标题
朝着数据驱动的随机预测控制
Towards data-driven stochastic predictive control
论文作者
论文摘要
基于Willems等人的基本引理的数据驱动的预测控制。经常考虑用于测量噪声的确定性LTI系统。但是，在数据驱动的随机控制上几乎没有完成。在本文中，我们为LTI系统提出了一个数据驱动的随机预测控制方案，可能会受到可能无限的附加过程干扰。基于基本引理和利用多项式混乱扩展的随机扩展，我们构建了数据驱动的替代替代最佳控制问题（OCP）。此外，结合OCP初始条件的在线选择策略，我们为递归可行性和稳定性提供了足够的条件，以构成所提出的数据驱动的预测控制方案。最后，两个数值示例说明了所提出的方案的功效和闭环特性，用于由不同分布控制的过程干扰。
Data-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little has been done on data-driven stochastic control. In this paper, we propose a data-driven stochastic predictive control scheme for LTI systems subject to possibly unbounded additive process disturbances. Based on a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions, we construct a data-driven surrogate Optimal Control Problem (OCP). Moreover, combined with an online selection strategy of the initial condition of the OCP, we provide sufficient conditions for recursive feasibility and for stability of the proposed data-driven predictive control scheme. Finally, two numerical examples illustrate the efficacy and closed-loop properties of the proposed scheme for process disturbances governed by different distributions.
