Learning-based control of multiple connected vehicles in the mixed traffic by adaptive dynamic programming

Tong Liu, Leilei Cui, Bo Pang, Zhong Ping Jiang

Research output: Contribution to journalConference articlepeer-review


The emergence of connected and autonomous vehicles (CAVs) has increased opportunities to mitigate the traffic congestion, improve safety and reduce accidents. In this paper, we consider the mixed traffic case with multiple heterogeneous human-driven vehicles and multiple CAVs on freeways. Under mild conditions, the stabilizability of the overall system is proved. With the tracking errors of relative distance and velocity as the states, we design an input-to-state stabilizing controller that solves a linear quadratic regulator problem by means of reinforcement learning and adaptive dynamic programming techniques. The priori knowledge of the vehicle network model is not needed. For a string of connected human-driven and automated vehicles, we give the sufficient conditions to guarantee the general string stability. The proposed learning-based control methodology is validated by means of simulation results.

Original languageEnglish (US)
Pages (from-to)370-375
Number of pages6
Issue number14
StatePublished - 2021
Event3rd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems MICNON 2021 - Tokyo, Japan
Duration: Sep 15 2021Sep 17 2021


  • Adaptive Dynamic Programming
  • Connected and Autonomous Vehicles
  • Stabilizability
  • String Stability

ASJC Scopus subject areas

  • Control and Systems Engineering


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