Nonlinear spacing policies for vehicle platoons: A geometric approach to decentralized control

Wijnbergen, Paul; Jeeninga, Mark; Besselink, Bart

doi:10.1016/j.sysconle.2021.104954

Cited by 16 publications

(8 citation statements)

References 29 publications

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“…where, Pi is the solution of the observer algebraic Riccati equation (ARE). 0 = 𝐴 𝑖 𝑇 𝑃 𝑖 + 𝑃 𝑖 𝐴 𝑖 + 𝑄 − 𝑃 𝑖 𝐶 𝑖 𝑇 𝑅 −1 𝐶 𝑖 𝑃 𝑖 (11) with Q and R being positive definite matrices. By considering the vehicle dynamics (1) matrix Ai can be written as: The selection of observer gain depends on the chosen values of Q and R. This selection represents a trade-off between estimation performance and a reasonable observer signal.…”

Section: Distributed Pi Controller Based On Cooperative Observermentioning

confidence: 99%

“…The first option is usually known as constant spacing policy (CSP) as used by Qiang et al [8] and Li et al [9], while the second option can be implemented with the concept of constant time heading (CTH) as used by Gaagai and Horn [10]. These two spacing policies are the most commonly used in vehicle platoon development research, although several other spacing policies also exist, such as the non-linear spacing policy [11] and the delay-based spacing policy [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synchronization of Heterogeneous Vehicle Platoon Using Distributed PI Controller Designed Based on Cooperative Observer

Prayitno,

Indrawati,

Yusuf

2024

MMEP

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Section: Distributed Pi Controller Based On Cooperative Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronization of Heterogeneous Vehicle Platoon Using Distributed PI Controller Designed Based on Cooperative Observer

Prayitno,

Indrawati,

Yusuf

2024

MMEP

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“…The work [5] has shown that also disturbance decoupling is impossible without communication. Disturbance decoupling refers to making a controlled output (typically the spacing error) independent of a disturbance (e.g., the control input of the preceding vehicle): the interest in studying disturbance decoupling is that it automatically guarantees string stability for suitably chosen spacing policy [5], [6].…”

Section: Output-feedback Design Of Longitudinalmentioning

confidence: 99%

Output-Feedback Design of Longitudinal Platooning With Adaptive Disturbance Decoupling

Liu

Besselink

Baldi

et al. 2022

IEEE Control Syst. Lett.

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In longitudinal platooning, 'disturbance decoupling' refers to the problem of making the intervehicle spacing independent of the disturbance input from the preceding vehicle, while 'adaptive' refers to handling vehicle parametric uncertainty such as engine time constant. This letter constructs a platooning architecture to achieve disturbance decoupling and adaptive goals without absolute position and relative velocity feedback, required in the state of the art. The interest in targeting disturbance decoupling is to have string stability (attenuation of disturbances along the platoon) as a by-product, as well as a decentralized design that helps handling parametric uncertainty. Stability analysis is provided along with numerical simulations.

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“…In [38], the stability of a vehicle platoon network with a ring coupling graph and path graph in the presence of time delays is studied. The spacing policy in [39], [40] specifies the desired distance between vehicles to ensure that all vehicles asymptotically track a group of heterogeneous mobiles.…”

Section: Introductionmentioning

confidence: 99%

Balancing Agility and Communication: Denser Networks Require Faster Agents

Huang,

de Oliveira,

Murugan

et al. 2023

IEEE Open J. Control. Syst.

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This paper delves into the challenges of ensuring stability (in some sense) and robustness in large-scale second-order consensus networks (SOCNs) and autonomous vehicle platoons in the discrete-time domain. We propose a graph-theoretic methodology for designing a state feedback law for these systems in a discrete-time framework. By analyzing the behavior of the solutions of the networks based on the algebraic properties of the Laplacian matrices of the underlying graphs and on the value of the update cycle (also known as the time step) of each vehicle, we provide a necessary and sufficient condition for the stability of a linear second-order consensus network in the discrete-time domain. We then perform an H 2 -based robustness analysis to demonstrate the relationship between the H 2 -norm of the system, network size, connectivity, and update cycles, providing insights into how these factors impact the convergence and robustness of the system. A key contribution of this work is the development of a formal framework for understanding the link between an H 2 -based performance measure and the restrictions on the update cycle of the vehicles. Specifically, we show that denser networks (i.e., networks with more communication links) might require faster agents (i.e., smaller update cycles) to outperform or achieve the same level of robustness as sparse networks (i.e., networks with fewer communication links) -see Figure 1. These findings have important implications for the design and implementation of large-scale consensus networks and autonomous vehicle platoons, highlighting the need for a balance between network density and update cycle speed for optimal performance. We finish the paper with results from simulations and experiments that illustrate the effectiveness of the proposed framework in predicting the behavior of vehicle platoons, even for more complex agents with nonlinear dynamics, using Quanser's Qlabs and Qcars.

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Nonlinear spacing policies for vehicle platoons: A geometric approach to decentralized control

Cited by 16 publications

References 29 publications

Synchronization of Heterogeneous Vehicle Platoon Using Distributed PI Controller Designed Based on Cooperative Observer

Synchronization of Heterogeneous Vehicle Platoon Using Distributed PI Controller Designed Based on Cooperative Observer

Output-Feedback Design of Longitudinal Platooning With Adaptive Disturbance Decoupling

Balancing Agility and Communication: Denser Networks Require Faster Agents

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