论文标题
数据驱动的随机输出反馈预测控制：通过插值初始条件的递归可行性
Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions
论文作者
论文摘要
该论文研究了受到随机干扰的线性系统的数据驱动输出反馈预测控制。该方案依赖于随机最佳控制问题（OCP）的合适数据驱动的重新制定的递归解决方案，该重新构造的输入和输出的统计分布可以进行前瞻性预测和优化。我们的方法避免使用参数系统模型。取而代之的是，它基于先前记录的数据，该数据使用Willems基本引理的最近提出的随机变体。引理的随机变体适用于受高斯和非高斯性质的随机干扰的大量线性动力学。为了确保递归可行性，OCP的初始条件（包括有关过去输入和输出的信息）被视为OCP的额外决策变量。我们提供了足够的条件，可实现拟议方案的递归可行性和闭环实际稳定性以及性能界限。最后，一个数值示例说明了所提出的方案的功效和闭环特性。
The paper investigates data-driven output-feedback predictive control of linear systems subject to stochastic disturbances. The scheme relies on the recursive solution of a suitable data-driven reformulation of a stochastic Optimal Control Problem (OCP), which allows for forward prediction and optimization of statistical distributions of inputs and outputs. Our approach avoids the use of parametric system models. Instead it is based on previously recorded data using a recently proposed stochastic variant of Willems' fundamental lemma. The stochastic variant of the lemma is applicable to a large class of linear dynamics subject to stochastic disturbances of Gaussian and non-Gaussian nature. To ensure recursive feasibility, the initial condition of the OCP -- which consists of information about past inputs and outputs -- is considered as an extra decision variable of the OCP. We provide sufficient conditions for recursive feasibility and closed-loop practical stability of the proposed scheme as well as performance bounds. Finally, a numerical example illustrates the efficacy and closed-loop properties of the proposed scheme.
