论文标题
数据驱动的分布式随机模型预测控制具有闭环机会限制满意度
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
论文作者
论文摘要
不确定系统的分布式模型预测控制方法通常会遭受相当大的保守主义的影响，并且由于使用可符合分布式设计和优化方法的强大配方，因此只能忍受小的不确定性。在这项工作中，我们提出了一个分布式随机模型预测控制（DSMPC）方案，用于动态耦合的线性离散时间系统，受到无界的加性干扰，可能会在时间上及时相关。间接反馈配方可确保DSMPC问题的递归可行性，以及数据驱动，分布式和优化的无限制拧紧方法，可以在闭环控制过程中准确满足机会约束，从而解决了典型的保守主义来源。所提出的控制器的计算复杂性与名义分布式MPC相似。在模拟中为大规模数据中心的温度控制所证明了该方法，但受到随机变化的计算负载。
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and optimization methods. In this work, we propose a distributed stochastic model predictive control (DSMPC) scheme for dynamically coupled linear discrete-time systems subject to unbounded additive disturbances that are potentially correlated in time. An indirect feedback formulation ensures recursive feasibility of the DSMPC problem, and a data-driven, distributed and optimization-free constraint tightening approach allows for exact satisfaction of chance constraints during closed-loop control, addressing typical sources of conservatism. The computational complexity of the proposed controller is similar to nominal distributed MPC. The approach is demonstrated in simulation for the temperature control of a large-scale data center subject to randomly varying computational loads.
