Distributed Data-Driven Predictive Control for Hybrid Connected Vehicle Platoons With Guaranteed Robustness and String Stability
Jingzheng Guo, Hongyan Guo, Jun Liu, Dongpu Cao, Hong Chen
Abstract
As a critical component of the Internet of Things, connected automated vehicles (CAVs) are progressively gaining attention for their benefits in terms of increased safety and reduced traffic congestion. In this article, a novel distributed data-driven model-predictive control (DDMPC) approach including feedforward for disturbance is proposed for cruise control of a hybrid platoon with a combination of human-operated and autonomous vehicles. By employing a predictor constructed from input/output data, predictive controllers are obtained without depending on the characteristic information of the system. A robustness analysis is performed with a combination of the input-to-state stability (ISS) theory with the sampled-data systems theory, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> -norm string stability is ensured by strict mathematical proof. In addition, we also discuss the asymptotic stability when the controller switches. CarSim simulation and bench experiment results verify that the DDMPC for connected vehicles can be robust to velocity disturbances and achieve satisfactory performance in ensuring string stability.