Grouping individual vehicles into platoons with a defined inter-vehicle spacing policy has been proven to greatly improve road throughput and reduce vehicles’ energy consumption. The emerging interest in distributed intervehicle
communication networks has provided new tools for further improvements of the performance of this platoon-based driving pattern. A leading control strategy of such vehicular cyber-physical systems is Cooperative Adaptive Cruise Control (CACC). However, a crucial limitation of the state-of-the-art is that string stability can be proven only when the vehicles in the platoon have identical driveline dynamics (homogeneous platoons). In this paper, we present a novel CACC strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability even with uncertain
heterogeneous platoons. Considering a one-vehicle look-ahead topology, we propose a Model Reference Adaptive Control augmentation: the control objective is to augment a baseline CACC, proven to be string stable in the homogeneous scenario, with an adaptive control term that compensates for each vehicle’s unknown driveline dynamics. Asymptotic convergence of the heterogeneous platoon to a string stable platoon is shown analytically for an appropriately designed reference model.
Simulations of the proposed CACC strategy are conducted to validate the theoretical analysis.
Original languageEnglish
Title of host publicationProceedings of the 2017 13th IEEE International Conference on Control & Automation (ICCA)
EditorsL. Liu, H. Lin
Place of PublicationPiscataway, NJ, USA
ISBN (Print)978-1-5386-2679-5
Publication statusPublished - 2017
EventICCA 2017 13th International Conference on Control & Automation - Ohrid, Macedonia, The Former Yugoslav Republic of
Duration: 3 Jul 20176 Jul 2017


ConferenceICCA 2017 13th International Conference on Control & Automation
Abbreviated titleICCA 2017
CountryMacedonia, The Former Yugoslav Republic of

    Research areas

  • Robust adaptive control, switched linear systems

ID: 31361538