Cooperative adaptive cruise control algorithms for vehicular platoons based on distributed model predictive control

Over the past decade, autonomous driving and driver assistance systems have become popular research topics with the aim of reducing traffic congestion, driver labor and rate of accidents. This has changed the requirements and priorities of today's transportation perception and reveals a need for an increased level of complexity. Changes in autonomous driving algorithms as a result of this differentiation have led the platoon system applications turn into a difficult control problem when evaluated together with bidirectional communication topologies and delays. Furthermore, the increased number of vehicles added to the platoon requires higher computational power. Using distributed controllers in such applications where response time is important helps us get more reliable results. The main objective of this thesis is to investigate Cooperative Adaptive Cruise Control Algorithms for Vehicular Platoons using Distributed Model Predictive Control (DMPC) under various communication links including unidirectional and bidirectional and also to propose a solution for a pre-known communication delay. Existing studies show that DMPC provides zero steady-state error with a fast response time under unidirectional communication topologies, however bidirectional links and communication delay effect are not adequately addressed. This thesis presents DMPC algorithms based on the information coming from the follower vehicles and delayed information received from the leader vehicle. It is shown that DMPC is the right tool to be utilized to both model and manage bidirectional communication. Simulation results demonstrate that the steady state error caused by the communication delay is successfully handled.