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Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies

IEEE Transactions on Intelligent Transportation Systems · 2015 · Vol. 17(1) · pp. 14–26
Yang ZhengShengbo Eben LiJianqiang WangDongpu CaoKeqiang Li

Abstract

In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as 0(1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results.

Traffic control and managementVehicular Ad Hoc Networks (VANETs)Autonomous Vehicle Technology and SafetyPlatoonNetwork topologyHomogeneousScalabilityComputer scienceStability (learning theory)Vehicular ad hoc networkFlow (mathematics)Topology (electrical circuits)Computer network

Funding

  • National Science Foundation
  • Wayne State University
  • National Natural Science Foundation of China
  • Tsinghua University
Citations
736
FWCI
38.46
field-weighted impact
References
47
Percentile
100%
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Citations per year
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