Uncertainty-Aware Dynamics Modeling and Data-Driven Robust Predictive Control for Mixed Vehicle Platoon
Hao Lyu, Yanyong Guo, Pan Liu, Ting Wang
Abstract
The effective control of connected and automated vehicles (CAVs) in mixed platoons offers fresh opportunities to optimize the emerging mixed traffic flow environment in the future. The goals of existing studies are modeling accuracy and control effectiveness. As a continued work of such a pursuit, this article develops a data-driven robust predictive control framework for the mixed platoon composed of CAVs and human driven vehicles (HDVs). A deep variational Koopman network (DVKoN) was proposed to learn the HDVs’ uncertainty-aware dynamics driving behavior based on the HighD dataset. A DVKoN-based robust predictive control framework (DVKoRPC) was designed for optimizing the mixed vehicle platoon. The DVKoRPC has two components, i.e., a nominal system and an error system. The nominal system, which integrates multiple DVKoNs based on the platoon formation, is used as the state predictive model for the mixed vehicle platoon. The error system is used to compensate for deviations and uncertainties within platoon operation. Moreover, the asymptotic stability of the mixed vehicle platoon with the DVKoRPC was proved using Lyapunov theory. The experimental was conducted to verify the proposed DVKoN and DVKoRPC. The results show that DVKoN can accurately predict the uncertain car-following behavior of HDVs in the mixed vehicle platoon. The proposed DVKoRPC can effectively alleviate traffic oscillations, improve traffic efficiency, and reduce fuel consumption.