文献类型：会议论文

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

通讯作者：Li, SE[1]

机构：[1]Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100027, Peoples R China;[3]Univ Sci & Technol Beijing, Dept Vehicle Engn, Beijing 100083, Peoples R China

第一机构：Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China

通讯机构：[1]corresponding author), Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China.

会议论文集：31st IEEE Intelligent Vehicles Symposium (IV)

会议日期：JUN 23-26, 2020

会议地点：ELECTR NETWORK

语种：英文

外文关键词：Model predictive control (MPC); time-splitting; alternating directions method of multipliers (ADMM); cross-node consensus

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 timesplitting technique.