Min-Max Model Predictive Vehicle Platooning With Communication Delay

Abstract

Vehicle platooning gains its popularity in improving traffic capacity, safety and fuel saving. The key requirements of an effective platooning strategy include keeping a safe intervehicle space, ensuring string stability and satisfying vehicular constraints. To meet these requirements, this paper proposes a distributed min-max model predictive control (MPC). One technical contribution is that the proposed MPC can guarantee input-to-state predecessor-follower string stability, in the presence of vehicle-to-vehicle communication delays and realistic constraints. Another technical contribution is the development of a new concept of input-to-state stability margin for analyzing the platooning system that is nonlinear under MPC. The proposed MPC is applicable to both homogeneous and heterogeneous platoons because only the point-mass vehicle model is needed. The proposed MPC also has reduced communication burden because each vehicle in the platoon only transmits its current acceleration to the adjacent follower. The design efficacy is verified by simulating a platoon composed of five vehicles under different uncertainties and communication delays.

Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

“…The mixed platoon has N + 1 vehicles, including the lead AV 0, the end AV N, and N − 1 HVs between them. The role of AV 0 is to ensure controllability of the entire platoon and assist the data collection for designing the proposed data-driven MPC for AV N. AV 0 is assumed to already have a well-tuned controller, e.g., the MPC in [30], to ensure the tracking of reference longitudinal velocity. The focus of this paper is to design the longitudinal acceleration commands of AV N to follow AV 0 by using the motion information from vehicles 0 to N − 1.…”

“…Feasibility of the problem in ( 23) also ensures that the predicted state is always enforced within the same constraint set Y. This means that Y is a constant terminal set of the proposed MPC [30]. Therefore, the MPC problem in ( 23) is feasible at any time t ≥ t 0 , if it is feasible at the first time instance t 0 .…”

“…To address the above challenges, this paper aims to develop a data-driven MPC for mixed platoons with unknown HV model parameters, unknown propulsion time delays and measurement noise. Due to its capability of real-time optimization and explicit constraint handling, MPC has been widely used for platoons of pure AVs [5]- [7], [9], [30] and also for mixed platoon with known HV models [22]. In principle, the MPC design relies on a known platoon model to predict the future platoon trajectory under the candidate control sequence.…”

Coalitional Distributed Model Predictive Control Strategy for Vehicle Platooning Applications

“…To verify that the platoon is string stable, the property (11) was used a posteriori, similarly with the work in [35], by analysing the corresponding ∆d i values obtained for each vehicle from the platoon. This was performed by computing the values for every two consecutive vehicles in the platoon, which according to (11), must be smaller or equal to 1, to ensure that the errors do not propagate towards the end of the platoon. Since the simulated platoon does not have a leader vehicle, the first investigation was performed between vehicles V 2 and V 3 by firstly computing 3 .…”

“…A vehicle platoon application is envisioned, consisting of four vehicles, dynamically coupled through the states. The model takes into account the dynamics of the vehicle, as opposed to the DMPC algorithms provided in [11][12][13], which were tested using a point-mass vehicle model. Each vehicle is locally controlled by an agent connected through an uni-directional channel, in a predecessor-following communication topology.…”