Compensation of Communication Delays in a Cooperative ACC System

Xing, Haitao; Ploeg, Jeroen; Nijmeijer, Henk

doi:10.1109/tvt.2019.2960114

Cited by 79 publications

(42 citation statements)

References 51 publications

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“…In case (b) a new time delay for each vehicle is drawn from a uniform distribution for each simulation run. The upper limit of the random time delay is set to 1 s, which is well above the time delays typically considered in the literature [33][34][35] and therefore covers all relevant scenarios. Case (c) is a hypothetical situation in which the controller should not be operated in general and it could not be called CACC any more, because there is no cooperation between the platoon members.…”

Section: Optimisation Resultsmentioning

confidence: 99%

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

2022

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Cooperative adaptive cruise control systems have the potential to improve fuel efficiency and safety. However, due to the large amount of uncertainties, which are encountered in platooning applications, typical controller calibrations are often not reliable. Therefore, in order to ensure a satisfying performance in the presence of various information topologies and relevant uncertainties such as errors in data transmission or extreme manoeuvres of the lead vehicle, a risk-averse stochastic optimisation approach for controller calibration is suggested and demonstrated for a pre-existing control scheme. Realistic vehicle dynamics simulation experiments with a prescribed set of uncertainties, such as transmission delays and different vehicle parameters, are performed. The results show that the collision probability and energy consumption are reduced by the risk-averse calibration of the controller and its spacing policy compared with classical calibration which assumes perfect communication.

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Section: Optimisation Resultsmentioning

confidence: 99%

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

2022

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“…Furthermore, the proposed Eco-ACC is model-free and real-time, and can be robust in different car-following scenarios. With the rapid development of vehicle-to-vehicle communication technology, the cooperative ACC has attracted a lot of researchers due to its better control performance than the ACC [31]- [33]. In the future, the ADHDP-based method will be used to develop a novel CACC strategy for connected and automated vehicles to further improve the control performance.…”

Section: Discussionmentioning

confidence: 99%

Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario

et al. 2021

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In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric vehicles (EVs), and improve the energy economy, an ADHDP-based economic adaptive cruise control (Eco-ACC) strategy for EVs in car-following scenarios is proposed in this paper. First, the longitudinal dynamics of EVs is modeled, and the control objectives are presented; then, the actor-critic structure of ADHDP is introduced, and the policy iteration formulas of the critic and actor networks in the ADHDP framework are given; finally, after the state variables, control variables, unity function and value function are determined, the ADHDP-based Eco-ACC strategy for EVs is designed. Extensive simulation results under different driving cycles show that the proposed Eco-ACC strategy can not only ensure vehicle safety, improve riding comfort and reduce energy consumption, but also significantly reduce the battery capacity loss and extend the battery life compared with the benchmark algorithm. In addition, the proposed Eco-ACC strategy is model-free and real-time, and can be robust in different car-following scenarios.

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“…In [14], the authors take into account the characteristics of wireless communication links, such as the sampling delays, and present a network-aware CACC approach. In other works [15]- [24], researchers have designed a variety of ACC/CACC control solutions by combining different mechanisms, such as multi-modeling [15], driving style recognition [16], direct yaw moment control [17], acceleration/control feedforward [18], linear quadratic regulator (LQR) [19], and information-delay compensation mechanisms [20]- [24]. Additionally, some researchers incorporate parametric uncertainties into the vehicle dynamics and aim to design robust linear feedback controllers for vehicle platooning by leveraging the classical Kharitonov theorem and the Hurwitz criterion [28].…”

Section: A Literature Reviewmentioning

confidence: 99%

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Zhou

Sheng

Duan

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) Manuscript

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Compensation of Communication Delays in a Cooperative ACC System

Cited by 79 publications

References 51 publications

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

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