This work regards the development of a distributed model predictive control strategy for large-scale systems, as centralized implementations often suffer from non-scalability. The decomposition of the overall system into minimally coupled subsystems as well as their coordination are based on optimality condition decomposition (OCD) and community detection. The OCD approach allows to solve the associated control subproblems in parallel in an iterative manner until the required degree of accuracy is attained. The proposed strategy is tested on two different systems, the quadruple-tank system and the Barcelona drinking water network, which allow to highlight the effectiveness of the approach.

这项工作涉及针对大型系统的分布式模型预测控制策略的开发，因为集中式实现通常会遭受不可扩展性的困扰。整个系统分解为最小耦合子系统及其协调是基于最佳条件分解（OCD）和社区检测的。OCD方法允许以迭代的方式并行解决关联的控制子问题，直到获得所需的精度。所提议的策略在两个不同的系统上进行了测试，即四缸系统和巴塞罗那饮用水网络，这可以突出该方法的有效性。