论文标题
动态耦合线性系统的数据驱动的分布式MPC
Data-driven distributed MPC of dynamically coupled linear systems
论文作者
论文摘要
在本文中，我们提出了一个数据驱动的分布式模型预测控制（MPC）方案，以稳定受到脱钩输入约束的动态耦合离散时间线性系统的起源。子系统解决的局部优化问题依赖于Willems等人对基本引理的分布式适应，从而仅使用未经显式模型知识的未经测量的输入输出数据来参数系统轨迹。对于本地预测，子系统依赖于传达的邻居所假定的轨迹。每个子系统都通过一致性约束来保证与这些轨迹的微小偏差。我们对所得的非文字分布式MPC方案提供了理论分析，包括递归可行性和（实际）稳定性的证明。最后，该方法成功应用于数值示例。
In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example.
