2020/05/28 by Carmen Amo Alonso, Nikolai Matni, Alonso, Carmen Amo +3
Chemistry · Engineering · #Advanced Control Systems Optimization #FOS: Electrical engineering #FOS: Mathematics #Fuel Cells and Related Materials #Metal-Organic Frameworks: Synthesis and Applications #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.13807
openalex publication_date 2020/05/28 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
An explicit Model Predictive Control algorithm for large-scale structured\nlinear systems is presented. We base our results on Distributed and Localized\nModel Predictive Control (DLMPC), a closed-loop model predictive control scheme\nbased on the System Level Synthesis (SLS) framework wherein only local state\nand model information needs to be exchanged between subsystems for the\ncomputation and implementation of control actions. We provide an explicit\nsolution for each of the subproblems resulting from the distributed MPC scheme.\nWe show that given the separability of the problem, the explicit solution is\nonly divided into three regions per state and input instantiation, making the\npoint location problem very efficient. Moreover, given the locality\nconstraints, the subproblems are of much smaller dimension than the full\nproblem, which significantly reduces the computational overhead of explicit\nsolutions. We conclude with numerical simulations to demonstrate the\ncomputational advantages of our method, in which we show a large improvement in\nruntime per MPC iteration as compared with the results of computing the\noptimization with a solver online.\n