Work

An Adaptive Longitudinal Platooning Design Based on Concurrent Learning

Year: 2023

Type: article

Abstract: This letter proposes a new adaptive longitudinal platooning strategy in the framework of concurrent learning. Adaptive refers to vehicles facing uncertainty in powertrain parameters via on-line estimation; concurrent learning refers to using both current and past data in the estimation. The proposed platooning strategy advances existing ones since convergence to the true powertrain parameters is guaranteed without imposing persistence of excitation on the vehicle behavior: it suffices the presence of a single non-zero data sample. Meanwhile, the concurrent learning proof we give advances existing ones since it takes into account an extra unknown gain in the error dynamics.

Source: IEEE Control Systems Letters

DOI: 10.1109/lcsys.2023.3333004

Authors: Qiuhao Wen, Di Liu, Jiwei Wang, Simone Baldi

Institutions: Southeast University, École Polytechnique Fédérale de Lausanne, Technical University of Munich, University of Groningen

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, National Natural Science Foundation of China

Awards: G1649157988, G4995428506, International Postdoc Academy in Science and Technology