文献类型：期刊文献

英文题名：Accelerated Convergence of Time-Splitting Algorithm for MPC using Cross-Node Consensus

作者：Maihemuti, Maierdanjiang[1]; Li, Shengbo Eben[1]; Li, Jie[1]; Gao, Jiaxin[3]; Li, Wenyu[1]; Sun, Hao[2]; Cheng, Bo[1]

第一作者：Maihemuti, Maierdanjiang

机构：[1] State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China; [2] College of Robotics, Beijing Union University, Beijing, 100027, China; [3] Department of Vehicle Engineering, University of Science and Technology Beijing, Beijing, 100083, China

第一机构：State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China

年份：2020

起止页码：566-571

外文期刊名：IEEE Intelligent Vehicles Symposium, Proceedings

收录：EI(收录号：20210509847514)

语种：英文

外文关键词：Topology - Forecasting - Predictive control systems

摘要：The splitting strategy over prediction horizon of model predictive control (MPC) has the potential to compute optimal action in a parallel way. However, such time-splitting algorithms often lead to very slow convergence speed because the state consensus only happens in each pair of adjacent nodes, i.e. a point-to-point topology. This paper proposes a generic cross-node consensus method to extend the shortcoming of limiting to point-to-point topology for the purpose of accelerating the convergence of time-splitting MPC. The cross-node consensus is realized by predicting the state transition from one node to another using plant prediction model, which can increase the information exchange efficiency in the prediction horizon. The time-splitting optimization algorithm is implemented by combing with alternating directions method of multipliers (ADMM). Simulations with autonomous driving show that this new algorithm significantly reduces the number of iterations in time-splitting MPC, averagely about 81% compared with classic time-splitting technique. ? 2020 IEEE.