论文标题
通过系统级合成的显式分布式和局部模型预测控制
Explicit Distributed and Localized Model Predictive Control via System Level Synthesis
论文作者
论文摘要
提出了用于大规模结构线性系统的显式模型预测控制算法。我们的结果基于分布式和局部模型预测控制（DLMPC），这是一种基于系统级综合（SLS）框架的闭环模型预测控制方案，其中仅需要在计算和实施控制动作的子系统之间交换局部状态和模型信息。我们为分布式MPC方案产生的每个子问题提供一个明确的解决方案。我们表明，鉴于问题的可分离性，显式解决方案仅分为每个状态和输入实例化的三个区域，从而使点位置问题非常有效。此外，鉴于局部限制，子问题的维度要比整个问题要小得多，这大大降低了显式解决方案的计算开销。我们以数值模拟结论来证明我们方法的计算优势，与在线计算优化的结果相比，我们在每次MPC迭代的运行时间均大大提高。
An explicit Model Predictive Control algorithm for large-scale structured linear systems is presented. We base our results on Distributed and Localized Model Predictive Control (DLMPC), a closed-loop model predictive control scheme based on the System Level Synthesis (SLS) framework wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We provide an explicit solution for each of the subproblems resulting from the distributed MPC scheme. We show that given the separability of the problem, the explicit solution is only divided into three regions per state and input instantiation, making the point location problem very efficient. Moreover, given the locality constraints, the subproblems are of much smaller dimension than the full problem, which significantly reduces the computational overhead of explicit solutions. We conclude with numerical simulations to demonstrate the computational advantages of our method, in which we show a large improvement in runtime per MPC iteration as compared with the results of computing the optimization with a solver online.
