Vehicle Platoon Control Research Articles

Ecological cooperative merging control of heterogeneous electric vehicle platoons.

Vehicle platooning improves energy savings via vehicle-to-vehicle (V2V) communication. Ecological cooperative adaptive cruise control (Eco-CACC) is implemented in platoons for merging task by using regrouped platoon models. The merging positions are selected in the middle and tail of an original platoon with a two-vehicle sub-platoon. The distributed nonlinear model predictive controller based on signal temporal logic (DNMPC-STL) approach is developed to model the Eco-CACC merging strategy. The performance of the Eco-CACC merging strategy is modeled by objective control for a predecessor-leader following (PLF) topology. The results demonstrate that merging positions located in the tail exhibit superior performance and can be used to improve stability, tracking performance, energy consumption efficiency and SOC of battery.

Cooperative control of mixed vehicle platoon based on pinning consensus of heterogeneous multi‐agent system

AbstractThis paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time‐delay and communication time‐delay based on the pinning consensus of heterogeneous multi‐agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non‐linear multi‐agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car‐following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi‐agent pinning consensus is proposed. Besides, the driver response time‐delay and communication time‐delay are introduced into the model, and a controller based on time‐delay heterogeneous multi‐agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time‐delay, communication time‐delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated.

Cooperative Spacing Control of Vehicle Platoon via Relative Output Feedback Considering Analog Fading Networks

ABSTRACTThe influence of analog fading channels on platoon control performance is studied via relative output feedback. The main goal is to determine the existence of a distributed output feedback controller capable of achieving mean square stability (MSS) for the underlying vehicle platoon systems over analog fading channels. First, the conditions, closely associated with the statistical characteristics of fading channels, for achieving platoon MSS are derived for three cases: undirected information flow (UIF) topology with identical fading channels, balanced directed information flow (BDIF) topology with identical fading channels, and general information flow topology (IFT) with nonidentical fading channels, respectively. These conditions explicitly reveal how the statistical characteristics of fading channels and IFT jointly affect vehicular stability. Second, in order to mitigate the impact of fading channels on platoon MSS, some feasible dynamic observer‐based controllers by using the relative output feedback, are proposed for each one of the former two situations by solving the corresponding modified Riccati inequalities (MRIs); and in the last situation, by employing edge Laplacian to model the vehicle platoon dynamics, a sufficient condition based on the solution of an LMI, and a feasible state‐based controller based on the solution of an MRI, are provided. Finally, simulations are carried out to compare three distinct fading channels, namely the Nakagami channel, Rician channel, and Rayleigh channel. The outcomes substantiate the superior and effective performance of the implemented control methods in guaranteeing the platoon MSS.

Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state–space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF–MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF–MPC controller outperforms the MPC and UKF–MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error and exhibits stronger robustness and stability.

Collaborative Control of Vehicle Platoon Based on Deep Reinforcement Learning

Collaborative Control of Vehicle Platoon Based on Deep Reinforcement Learning

Compensation control of commercial vehicle platoon considering communication delay and response lag

The real-time accurate control of vehicle is of great significance for the stability of platoon driving. To address the impact of random time-varying communication delays, communication packet loss, data disorder, and significant response lag of commercial vehicle on platoon control performance in non-ideal communication environment, a novel compensation control method for commercial vehicle platoon is proposed to overcome the abovementioned issues. The proposed method significantly improves the impact of communication issues and vehicle response lag on platoon control, ensuring platoon safety while reducing inter-vehicle spacing and spacing error, and maintaining platoon stability. In addition, this method combines the comprehensive compensation mechanism with the Distributed Model Predictive Controller (DMPC), avoiding the establishment of additional compensation control modules and effectively reducing the computational burden. The results under emergency braking condition show that, compared with the method without compensation, the proposed method significantly reduces inter-vehicle spacing and spacing error, reducing the average maximum spacing error from 29.25 m to 1.44 m, shortening the error convergence time of the platoon by 28 %, and ensuring platoon stability. Additionally, compared to the compensation method based on Kalman Filtering (KF) and Smith predictor, the proposed method can reduce the average maximum spacing error by 60 % while maintaining similar convergence time and platoon stability.

ROBUST OUTPUT FEEDBACK CONTROL FOR HETEROGENEOUS AUTONOMOUS VEHICLE PLATOONS

An heterogeneous vehicle platoon controller for autonomous vehicles is proposed in this paper. The traction force of each vehicle is controlled according to the state of the predecessor vehicle, which can be known by LiDAR/RADAR, and the state of the leader vehicle, which is received by vehicle-to-vehicle (V2V) communication. Platoon string stability is evaluated under Lyapunov and H8 conditions. An iterative procedure is proposed in order to deal with the non-convexity of the static output feedback control design problem. Simulation results demonstrate that the proposed heterogeneous vehicle platoon controller achieves a good overall performance without compromising the safety of any vehicle. The separation between each follower and its predecessor is kept in appropriate ranges, always close to the desired reference. Furthermore, the transient errors are not amplificated when propagated downstream the platoon, therefore string stability is assured. Keywords: Autonomous vehicles, heterogeneous vehicle platoon, vehicle platoon control, string stability, output feedback control, robust control, Linear Matrix Inequality

Observer‐based adaptive control of vehicle platoon with uncertainty and input constraints

AbstractThis study focuses on the highway platoon driving mode and proposes a distributed adaptive control algorithm based on an observer. Firstly, the adaptive observer is designed to compensate for the effect of unknown driving resistance and thus enhance the adaptation ability of the system to uncertainty. Secondly, an auxiliary system is introduced to specifically address actuator saturation constraints, ensuring the stability of the platoon driving in extreme conditions. Lastly, combining an event‐triggered mechanism, a control strategy is designed to achieve the stability of the entire platoon while maximizing the conservation of communication resources. The algorithm's viability and efficiency are confirmed through simulation outcomes.

Predictor and ESO-based adaptive tracking control of heterogeneous vehicle platoon

Predictor and ESO-based adaptive tracking control of heterogeneous vehicle platoon

An adaptive coupled control method based on vehicles platooning for intersection controller and vehicle trajectories in mixed traffic

AbstractConnected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

Distributed prescribed performance platoon control for heterogeneous vehicles with quadratic spacing policy based on ESO

Distributed prescribed performance platoon control for heterogeneous vehicles with quadratic spacing policy based on ESO

Predefined-Time Platoon Control of Unmanned Aerial Vehicle with Range-Limited Communication

In this paper, the predefined-time platoon control for multiple uncertain unmanned aerial vehicles (UAVs) under range-limited communication and external disturbance constraints is considered. A novel control scheme, which can guarantee communication connectivity, collision avoidance, and the predefined convergence time simultaneously, is proposed. To achieve disturbance robustness, an observer-based distributed control law is firstly proposed with a time-varying gain. Then, a radial basis function neural network (RBFNN) with an adaptive tuning law is applied to approximate uncertainties of the system. Under the time and error transformation techniques, uniformly ultimate boundedness (UUB) stability of the closed-loop system is guaranteed within predefined convergence time. Compared with the existing results, the proposed method allows the system to have UUB within any predefined time without depending on the initial conditions or system parameters. Finally, simulation results are presented to verify the derived theorem.

Vehicle Platoon Control Based on Third-Order Heterogeneous Model and Predictive Spacing Strategy

Vehicle Platoon Control Based on Third-Order Heterogeneous Model and Predictive Spacing Strategy

Data-Driven Distributed Vehicle Platoon Control for Heterogeneous Nonlinear Vehicle Systems

Data-Driven Distributed Vehicle Platoon Control for Heterogeneous Nonlinear Vehicle Systems

Delay Compensation Control of Heterogeneous Vehicle Platoons: A Principal-Agent Structure

Delay Compensation Control of Heterogeneous Vehicle Platoons: A Principal-Agent Structure

Consensus-Based Vehicle Platoon Control Under Periodic Event-Triggered Strategy

Consensus-Based Vehicle Platoon Control Under Periodic Event-Triggered Strategy

Robust Adaptive Heterogeneous Vehicle Platoon Control Based on Disturbances Estimation and Compensation

Robust Adaptive Heterogeneous Vehicle Platoon Control Based on Disturbances Estimation and Compensation

Longitudinal and lateral control of vehicle platoons using Laguerre-based and robust MPC with merge and exit maneuvers

This paper addresses the longitudinal and lateral control of platooning vehicles with the objective of maintaining safe vehicular distance and accomplishing lateral maneuvers. For the longitudinal control, a two-layer controller is proposed. In the first step, feedback linearization transform the nonlinear longitudinal dynamics of vehicles to a linear one for which Laguerre-based Model predictive control (LMPC) is designed in the second layer. The objective function of LMPC includes safe vehicular distance and control effort under state and input constraints. The proposed controller using Laguerre orthonormal functions for input signal parametrization, achieves the desired platoon control and dramatically decreases computation time. For lateral control, a robust MPC is developed to track the reference path in maneuvers of merging a new vehicle to the platoon or exiting a vehicle from it. The road bump is considered a source of uncertainties and is modeled with a bounded additive disturbance. The proposed longitudinal and lateral controller is then used in a high-level control strategy to manage merge and exit maneuvers. Besides extensive simulation results to verify the efficiency of the proposed strategy, experimental evaluation using the hardware-in-the-loop (HIL) test bench based on Raspberry Pi board and actual robots validated the plan’s viability in practice.

Mixed traffic system with multiple vehicle types and autonomous vehicle platoon: Modeling, stability analysis and control strategy

This paper focuses on platoon control of autonomous vehicles (AVs) equipped with V2X technology and the phenomenon that the large platoons are split into multiple sub-platoons due to the communication restrictions. At the same time, with the popularity of AVs and connected human-driven vehicles (CVs), this paper also carries out the research of mixed traffic flow mixed with AV platoons, CVs and regular human-driving vehicles (RVs). According to the characteristics of the different types of vehicles, this paper extends the corresponding car-following models based on intelligent driver model (IDM), and considers the functional degradation of AVs under different car-following modes. In this paper, the AVs are classified into the second-level degenerated AV (D2AV), the first-level degenerated AV (D1AV) and the normal functioning AV (NAV) based on the differences in car-following modes. The degradation of AVs is represented via the change of communication topologies and models. Linear stability analysis is conducted. Numerical simulation shows that the higher level of AV degradation, the worse the stability of traffic flow. Under the same conditions, the more sub-platoons and the larger maximum size of sub-platoons, the better the stability of the traffic flow. The increase in communication strength of AVs is also beneficial to stability of traffic flow. In order to compensate for degradation, an adaptive gain feedback control strategy is proposed. Simulation shows that the adaptive gain feedback strategy can effectively improve the ability of the traffic flow to resist disturbances, reduce the fluctuations of velocity and acceleration, and contribute to the stability of platoon system and traffic flow.

Distributed model predictive control of vehicle platoons under switching communication topologies

Abstract This paper studies the control problem for vehicle platoons under switching communication topologies, where the Predecessor-Following (PF)-failure topology is allowed. Firstly, we design a local optimization problem for each vehicle by using the state information of itself and its neighbouring vehicles, and then present Algorithm 1 based on Distributed Model Predictive Control (DMPC). By constructing a common Lyapunov function defined by the joint neighbourhood set, we derive sufficient conditions with respect to the weight matrices in the open-loop optimization problem and the switching topologies for ensuring the stability of the closed-loop system under Algorithm 1. Furthermore, we propose Algorithm 2 by introducing novel constraints into the local optimization problem for each vehicle, which utilize shrinking historical position errors to cope with PF-link failures, such that the string stability of the vehicle platoon is ensured. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed DMPC algorithms for the vehicel platoon under switching communication topologies.