In this paper we introduce an iterative Jacobi algorithm for solving distributed model predictive control (DMPC) problems, with linear coupled dynamics and convex coupled constraints. The algorithm guarantees stability and persistent feasibility, and we provide a localized procedure for constructing an initial feasible solution by constraint tightening. Moreover, we show that the solution of the iterative process converges to the centralized MPC solution. The proposed iterative approach involves solving local optimization problems consisting of only few subsystems, depending on the choice of the designer and the sparsity of dynamical and constraint couplings. The gain in the overall computational load compared to the centralized problem is balanced by the increased communication requirements. This makes our approach more applicable to situations where the number of subsystems is large, the coupling is sparse, and local communication is relatively fast and cheap. A numerical example illustrates the effects of the local problem size, and the number of iterations on convergence to the centralized solution.
Predictive Control Strategies for Sustaining Innovation Adoption on Multilayer Social Networks
KTH Royal Institute of Technology（皇家理工学院）；National Research Council of Italy, Politecnico di Torino（意大利国家研究委员会，都灵理工大学）；Institute of Science Tokyo（科学技术高等研究院）；The University of Tokyo（东京大学）
An Age-Structured Vaccination Strategy for Epidemic Containment: A Model Predictive Control Approach
University of Namur（南纳慕尔大学）；HEC - Management School of the University of Liège（列日大学高等商学院）；Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab（格勒诺布尔阿尔卑斯大学、法国国家科学研究中心、格勒诺布尔理工学院、GIPSA实验室）
