Paper Info
Title
Non-Iterative Distributed Model Predictive Control For Flexible Vehicle Platooning Of Connected Vehicles
Abstract
This work proposes a non-iterative distributed model predictive control (DMPC) scheme for flexible vehicle platooning with state and input constraints. The proposed DMPC scheme partitions the multi-vehicle system into alternate clusters belonging to two categories, and distributively solves constrained optimal control problems (COCPs) in a two-step manner at each sampling instant. At the first step, the scheme solves COCPs over one category of vehicle clusters that are disjoint. Then, at the second step, COCPs are solved over the other category with predicted states of common neighbors transmitted from the first step. Instead of applying compatibility constraints on all vehicles, only compatibility constraints of common neighbors of two neighboring clusters are required in the proposed scheme to ensure feasibility and closed-loop stability. The proposed DMPC law can be obtained by solving a semidefinite program (SDP). A flexible vehicle platooning example is simulated to illustrate the effectiveness of the proposed DMPC scheme.
Year
Venue
Field
2017
2017 AMERICAN CONTROL CONFERENCE (ACC)
Cluster (physics),Mathematical optimization,Disjoint sets,Optimal control,Compatibility (mechanics),Control theory,Computer science,Model predictive control,Control engineering,Vehicle dynamics,Vehicle platooning,Sampling (statistics)
DocType
ISSN
Citations 
Conference
0743-1619
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Peng Liu1192.24
Ümit Özgüner21014166.59