arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1909.10074math.OCcs.SYeess.SY
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基于系统级综合的分布式局部模型预测控制

Distributed and Localized Model Predictive Control via System Level Synthesis

Carmen Amo Alonso, Nikolai Matni

摘要:

针对大规模结构化线性系,提出了一种分布式局部模型预测控制(DLMPC)算法,其中子系统之间只需要交换局部状态和模型信息,就可以计算和执行控制动作。我们使用的系统级综合(SLS)的框架重新制定的MPC问题作为一个最优化问题的闭环系统响应,并表明,这使我们能够自然地施加本地化的子控制器之间的通信约束,这样,只有本地状态和系统模型信息需要交换的计算和实施的闭环MPC控制策略。特别是,我们表明,所得到的最优化问题的结构可以利用开发一个交替方向法的乘数(ADMM)为基础的算法,允许分布式和本地化的计算控制决策。此外,我们的方法可以容纳的约束条件和目标函数,耦合不同的子系统的行为,只要耦合系统能够直接相互通信,允许更广泛的一类MPC问题,通过分布式优化来解决。我们的结论与数值模拟证明我们的方法的有用性,特别是,我们证明了计算复杂性的DLMPC中的每个子系统解决的子问题是独立的全球系统的大小。

英文摘要:

We present the Distributed and Localized Model Predictive Control (DLMPC) algorithm for large-scale structured linear systems, wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the System Level Synthesis (SLS) framework to reformulate the MPC problem as an optimization problem over closed loop system responses, and show that this allows us to naturally impose localized communication constraints between sub-controllers, such that only local state and system model information needs to be exchanged for both computation and implementation of closed loop MPC control policies. In particular, we show that the structure of the resulting optimization problem can be exploited to develop an Alternating Direction Method of Multipliers (ADMM) based algorithm that allows for distributed and localized computation of control decisions. Moreover, our approach can accommodate constraints and objective functions that couple the behavior of different subsystems, so long as the coupled systems are able to communicate directly with each other, allowing for a broader class of MPC problems to be solved via distributed optimization. We conclude with numerical simulations to demonstrate the usefulness of our method, and in particular, we demonstrate that the computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system.

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