Distributed model predictive control (MPC) has been proven a successful method in regulating the operation of large-scale networks of constrained dynamical systems. This paper is concerned with cooperative distributed MPC in which the decision actions of the systems are usually derived by the solution of a system-wide optimization problem. However, formulating and solving such large-scale optimization problems is often a hard task which requires extensive information communication among the individual systems and fails to address privacy concerns in the network. Hence, the main challenge is to design decision policies with a prescribed structure so that the resulting system-wide optimization problem to admit a loosely coupled structure and be amendable to distributed computation algorithms. In this paper, we propose a decentralized problem synthesis scheme which only requires each system to communicate sets which bound its states evolution to neighboring systems. The proposed method alleviates concerns on privacy since this limited communication scheme does not reveal the exact characteristics of the dynamics within each system. In addition, it enables a distributed computation of the solution, making our method highly scalable. We demonstrate in a number of numerical studies, inspired by engineering and finance, the efficacy of the proposed approach which leads to solutions that closely approximate those obtained by the centralized formulation only at a fraction of the computational effort.
翻译:事实证明,分散的模型预测控制(MPC)是管理有限动态系统大规模网络运作的成功方法。本文件涉及合作分布式的多用途多用途多用途多用途多用途多用途多用途多用途多用途多用途多功能多功能多功能多功能多功能多功能多功能多功能多功能问题,而系统的决定行动通常是通过解决全系统优化问题的办法产生。然而,制定和解决这种大规模优化问题往往是一项艰巨的任务,需要各个系统之间广泛交流信息,无法解决网络中的隐私问题。因此,主要的挑战在于设计具有规定结构的决策政策,以便导致全系统范围优化问题,从而导致采用松散的组合结构,并可以修改分布式计算算法。我们在本文件中提议了一个分散的问题综合计划,它只要求每个系统将其状态演变与相邻系统连接的组合进行沟通。拟议的方法缓解了对隐私的关切,因为这一有限的通信办法并未揭示每个系统动态的确切特点。此外,它能够对解决方案进行分散的计算,使我们的方法具有高度可伸缩性。我们在一系列数字研究中,在工程和财政的启发下,展示了拟议方法的功效,其效率导致仅通过集中计算方法获得的计算方法的解决方案。