Search Results (70)

A decentralized control problem for vehicular platoon systems with heterogeneous dynamic behaviors is investigated in this paper. To simplify the controller design, a longitudinal model is established as an interconnected form. On this basis, a series of decentralized state feedback controllers are designed to ensure the individual stability, string stability and connective stability of the vehicular platoon system. Then, a new scenario in which additional vehicles are added to the platoon is also considered by developing an expanding construction system (ECS) based on the proposed longitudinal model. As a result, a corresponding controller can be designed as a new one of the decentralized controllers without changing the original control laws of the interconnected system. The stability conditions are presented with rigorous analysis by virtue of linear matrix inequality (LMI) for the interconnected system and the ECS. Simulation results are carried out to demonstrate the effectiveness of the proposed decentralized tracking controllers. Full article

In this paper, we propose systematic controller design guidelines to ensure both individual vehicle stability and string stability in cooperative adaptive cruise control (CACC)-based platoon systems, assuming a homogeneous platoon where all vehicles share identical dynamic models. We rigorously demonstrate that the limitation of conventional adaptive cruise control (ACC) in maintaining the target inter-vehicle distance can be effectively overcome by incorporating the desired acceleration of the preceding vehicle as a static feedforward input. Furthermore, by formulating transfer functions in the frequency domain, we analytically derive the conditions required to ensure both individual vehicle stability and string stability of the CACC system. Building on this insight, we propose a practical and theoretically well-founded design guideline for determining the proportional, derivative, and feedforward gains of control input under a constant time gap spacing policy. The proposed guidelines are validated through simulations conducted in a realistic platooning scenario involving multiple vehicles. Full article

This work studies the issue of distributed fault-tolerant control for a vehicle platoon with actuator faults, saturation, and external disturbances. As the degrees of wear, age, and overcurrent of a vehicle actuator might change during the working process, it is more practical to consider the actuator faults to be time-varying rather than constant. Considering a situation in which actuator faults may cause partial actuator effectiveness loss, a novel adaptive updating mechanism is developed to estimate this loss. A new nonlinear observer is proposed to estimate external disturbances without requiring us to know their upper bounds. Since non-zero initial spacing errors (ISEs) may cause instability of the vehicle platoon, a novel exponential spacing policy (ESP) is devised to mitigate the adverse effects of non-zero ISEs. Based on the developed nonlinear observer, adaptive updating mechanism, radial basis function neural network (RBFNN), and the ESP, a novel nonlinear observer-based distributed adaptive fault-tolerant control strategy is proposed to achieve the objectives of platoon control. Lyapunov theory is utilized to prove the vehicle platoon’s stability. The rightness and effectiveness of the developed control strategy are validated using a numerical example. Full article

This study proposes an adaptive car-following model based on the unscented Kalman filter algorithm to enable coordinated speed control in vehicle platoons and to address key limitations present in conventional car-following models. Traditional models generally assume a fixed maximum speed within the optimal velocity function, which constrains effective platoon speed regulation across road segments with varying speed limits and lacks adaptability to dynamic scenarios such as changes in the platoon leader’s speed or substitution of the lead vehicle. The proposed adaptive model utilizes state estimation based on the unscented Kalman filter to dynamically identify each vehicle’s maximum achievable speed and to adjust inter-vehicle constraints, thereby enforcing a unified speed reference across the platoon. By estimating these maximum speeds and transmitting them to individual follower vehicles via vehicle-to-vehicle communication, the model promotes smooth acceleration and deceleration behavior, reduces headway variability, and mitigates shockwave propagation within the platoon. Simulation studies—covering both single-leader acceleration and intermittent acceleration scenarios—demonstrate that, compared with conventional car-following models, the adaptive model based on the unscented Kalman filter achieves superior speed synchronization, improved headway stability, and smoother acceleration transitions. These enhancements lead to substantial improvements in traffic flow efficiency and string stability. The proposed approach offers a practical solution for coordinated platoon speed control in intelligent transportation systems, with promising application prospects for real-world implementation. Full article

The article focuses on the issue of the sliding mode control of a vehicle platoon with matched and mismatched disturbances. A novel finite-time disturbance observer-based sliding mode control scheme is developed to effectively mitigate the adverse impact of disturbances and achieve the control goals of a platoon. As matched and mismatched disturbances might decrease the control performance or even cause the instability of a vehicle platoon, a finite-time disturbance observer (FTDO) is designed to effectively reduce the effects of both types of disturbances. Unlike previous studies, the proposed FTDO in this article has the capability to directly estimate disturbances without the need to know the precise upper bounds of the disturbances. A feedforward compensation term, derived from disturbance estimation, is incorporated into the FTDO-based sliding mode control scheme to solve the issue of the degradation of control performance. The controlled vehicle platoon’s stability is proven through the Lyapunov approach, which means that the control goals of the platoon can be achieved under the developed FTDO-based sliding mode control scheme. Finally, a numerical example is conducted to confirm the efficacy of the developed control scheme. Full article

This paper proposes a practical design guideline for selecting control parameters in adaptive cruise control (ACC) systems to ensure both individual vehicle stability and string stability in vehicle following systems with homogeneous longitudinal dynamics. The primary control objective is to regulate spacing errors under a constant time-gap policy, which is commonly adopted in ACC applications. By employing a simple proportional-derivative (PD) controller, we present a clear methodology for tuning the proportional and derivative gains. The proposed approach demonstrates that string stability can be effectively achieved using this straightforward control structure, making it highly applicable for assisting practitioners in selecting appropriate parameters for real-world platooning scenarios. We provide a rigorous analysis of the necessary and sufficient conditions for selecting PD gains, along with practical guidelines for implementation. The effectiveness of the design guideline is further validated through simulations conducted in realistic driving scenarios. Full article

In this paper, we study the stability of the stochastically heterogeneous traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Taking the stochasticity of vehicle arrivals and behaviors into account, a general robust $H_{\infty }$ platoon controller is proposed to address the communication delay and unexpected disturbances such as prediction or perception errors on HDV motions. To simplify the problem complexity from a stochastically heterogeneous traffic flow to multiple long vehicle control problems, three types of sub-platoons are identified according to the CAV arrivals, and each sub-platoon can be treated as a long vehicle. The car-following behaviors of HDVs and CAVs are simulated using the optimal velocity model (OVM) and the cooperative adaptive cruise control (CACC) system, respectively. Later, the robust $H_{\infty }$ platoon controller is designed for a pair of a CAV long vehicle and an HDV long vehicle. The time-lagged system and the closed-loop system are formulated and the $H_{\infty }$ state feedback controller is designed. The robust stability and string stability of the heterogeneous platoon system are analyzed using the $H_{\infty }$ norm of the closed-loop transfer function and the time-lagged bounded real lemma, respectively. Simulation experiments are conducted considering various settings of platoon sizes, communication delays, disturbances, and CAV penetration rates. The results show that the proposed $H_{\infty }$ controller is robust and effective in stabilizing disturbances in the stochastically heterogeneous traffic flow and is scalable to arbitrary sub-platoons in various CAV penetration rates in the heterogeneous traffic flow of road vehicles. The advantages of the proposed method in stabilizing heterogeneous traffic flow are verified in comparison with a typical car-following model and the linear quadratic regulator. Full article

This study proposes a stability index to quantitatively evaluate the psychological stability of drivers during truck platooning. Truck platooning is a technique in which a manually driven lead truck is followed by automated trucks using V2X communication and onboard sensors. While significant technical advances have been made in truck platooning, research on drivers’ psychological comfort remains limited. Due to the shorter intervehicle time gaps compared to conventional trucking, truck platooning raises concerns regarding drivers’ psychological stability. The proposed index quantifies this aspect and is validated using a driving simulator. Both the stability index calculations and the survey consistently indicate that when the time gap decreases to 0.6 s (approximately 15 m) or less, drivers’ psychological stability deteriorates. However, increasing the time gap beyond 0.6 s does not significantly improve it. Furthermore, this study investigates the effect of the see-through functionality, which provides real-time front-view footage of the preceding vehicle to the following driver. The results confirm that this functionality enhances psychological stability, even under a short time gap. The stability index should serve as a practical indicator for designing truck platooning systems that consider drivers’ psychological stability and may be extended to various advanced driving technologies, including general vehicle platooning. Full article

The emergence of autonomous vehicles offers the potential to eliminate traditional traffic lanes, enabling vehicles to navigate freely in two-dimensional spaces. Unlike conventional traffic constrained by physical lanes, autonomous vehicles rely on real-time data exchange within platoons to adopt cooperative movement strategies, similar to synchronized flocks of birds. Motivated by this paradigm, this paper introduces an innovative traffic flow model based on the principles of particle swarm intelligence. In the proposed model, each vehicle within a platoon is treated as a particle contributing to the collective dynamics of the system. The motion of each vehicle is determined by the following two key factors: its local optimal velocity, influenced by the preceding vehicle, and its global optimal velocity, derived from the average of the optimal velocities of M vehicles within its observational range. To implement this framework, we develop a novel particle swarm optimization algorithm for autonomous vehicles and rigorously analyze its stability using linear system stability theory, as well as evaluate the system’s performance through four distinct indices inspired by traditional control theory. Numerical simulations are conducted to validate the theoretical assumptions of the model. The results demonstrate strong consistency between the proposed swarm intelligent model and the Bando model, providing evidence of its effectiveness. Additionally, the simulations reveal that the stability of the traffic flow system is primarily governed by the learning parameters $c_1$ and $c_2$, as well as the field of view parameter M. These findings underscore the potential of the swarm intelligent model to improve traffic flow system dynamics and contribute to the broader application of autonomous traffic systems management. In addition, it is worth noting that this paper explores the operational control of an AV platoon from a theoretical perspective, without fully considering passenger comfort, as well as “soft” instabilities (vehicles joining/leaving) and “hard” instabilities (technical failures/accidents). Future research will expand on these related aspects. Full article

The rapid proliferation of vehicles globally presents significant challenges to road transportation efficiency and safety, including accidents, emissions, energy utilization, and road management. Autonomous vehicle platooning emerges as a promising solution within intelligent transportation systems, offering benefits like reduced fuel consumption and emissions, and optimized road use. However, implementing autonomous vehicle platooning faces obstacles such as stability under disturbances, safety protocols, communication networks, and precise control. This paper proposes a novel control strategy coordinated Kalman observer–Linear Quadratic Regulator (CKO-LQR) to ensure platoon formation stability in the presence of disturbances. The disturbances considered include vehicle movements, sensor noise, and communication delays, with the leading vehicle’s movement serving as the commanding signal. The proposed controller maintains a constant inter-gap distance between vehicles despite the disturbances utilizing a coordinated Kalman observer to estimate preceding vehicle movements. A comparative analysis with conventional PID controllers demonstrates superior performance in terms of faster settling times and robustness against disturbances. This research contributes to enhancing the efficiency and safety of autonomous vehicle platooning systems. Full article

Population growth and rising mobility demands have significantly increased traffic congestion and extended travel times. To address these challenges, traffic flow can be optimized by organizing vehicles into clusters, known as vehicle platoons, where cars travel closely together in a co-ordinated manner. Although the concept of vehicle platoon control holds great promise for improving traffic efficiency and reducing fuel consumption, its practical implementation faces several issues. Variations in vehicle specifications, such as differences in mass, can destabilize platoons and negatively impact overall performance. This paper introduces a novel method to maintain stable vehicle co-ordination despite such uncertainties. The proposed method utilizes a static output feedback control strategy, which simplifies the communication architecture within the platoon, as only partial state information from each vehicle is required. The simulation results demonstrate that this method effectively minimizes spacing errors and ensures platoon stability. This approach not only enhances safety but also improves traffic flow, making it a viable strategy for future intelligent transportation systems. Full article

This article proposes event-triggered cruise control in platoons of connected automated vehicles (CAVs) with heterogeneous input limitations. A distributed control protocol is developed to ensure the stability and performance of the platoon, explicitly addressing varying levels of input saturation among vehicles. To further enhance communication efficiency, a centralized event-triggered mechanism is introduced, activating control updates only when necessary, effectively preventing Zeno behaviors through a predefined threshold. The proposed approach not only achieves global asymptotic stability but also significantly reduces communication demands, making it suitable for real-world driving conditions characterized by input constraints. Simulation results validate the effectiveness and robustness of the proposed control strategy, demonstrating its potential for practical implementation in intelligent transportation systems. Full article

This paper suggests an adaptive car-following strategy for autonomous connected vehicles (ACVs) that integrates a robust controller with an extended disturbance estimator (EDE) and reinforcement learning (RL) to improve performance in dynamic traffic environments. Traditional car-following methods struggle to handle external disturbances and uncertainties in vehicle dynamics. The suggested method addresses this by dynamically adjusting the EDE gain using RL, enabling the system to optimize its control strategy in real time continuously. Simulations were conducted in two scenarios, a single following vehicle and two following vehicles, each tracking a leading vehicle. Results showed significant improvements in velocity tracking, with the RL-based control method reducing velocity error by over 50% compared to conventional approaches. The technique also led to smoother acceleration control, enhancing stability and driving comfort. Quantitative metrics, such as total reward, velocity error, and acceleration magnitude, indicate that the suggested EDE-RL-based strategy provides a robust and adaptable solution for autonomous vehicle control. These findings indicate that RL, combined with robust control, can improve the performance and safety of ACV systems, making it suitable for broader applications in autonomous vehicle platooning and complex traffic scenarios, including vehicle-to-vehicle (V2V) communication. Full article

Platooning of vehicular systems has been considered an effective solution for alleviating traffic congestion and reducing energy consumption. Because of limitations in onboard sensors, the measurement system inevitably suffers from measurement delays and noise, yet it receives insufficient attention. In this article, to deal with the measurement delays and noise while improving convergence performance, the platoon control problem of vehicular systems is studied under the nonsingular terminal sliding mode control (NTSMC) framework. A sliding mode observer (SMO) is proposed to estimate the states affected by measurement delays and noise. A distributed NTSMC scheme is developed for the platooning of the vehicular systems and ensures the convergence of the sliding mode surface affected by measurement delays and noise. One salient feature of the proposed SMO is that it can handle time-varying measurement delays rather than constant ones. Moreover, the control law is free of initial spacing error conditions under the employed coupled spacing policy. Numerical simulations are finally provided to demonstrate the effectiveness and efficiency of the proposed algorithm. Full article

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. Full article