Distributed Data-Driven Model Predictive Control for Heterogeneous Vehicular Platoon With Uncertain Dynamics

被引：4

作者：

Wu, Yanhong ^{[1
]}

Zuo, Zhiqiang ^{[1
]}

Wang, Yijing ^{[1
]}

Han, Qiaoni ^{[1
]}

Hu, Chuan ^{[2
]}

机构：

[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin Key Lab Intelligent Unmanned Swarm Techno, Tianjin 300072, Peoples R China

[2] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2023年 / 72卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Vehicle dynamics; Predictive models; Aerodynamics; Heuristic algorithms; DC motors; Predictive control; Delays; Uncertain dynamics; heterogeneous vehicular platoon; distributed data-driven model predictive control; subspace identification; STRING STABILITY; VEHICLE; TRACKING; MPC;

D O I：

10.1109/TVT.2023.3262705

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

To alleviate the adverse effects of heterogeneous vehicular platoon (HVP) with uncertain dynamics, a distributed data-driven model predictive control (DDMPC) strategy is proposed in this paper. A data-driven model is established with subspace identification using the input-output (I/O) vehicle trajectory. We integrate the data-driven model with the distributed model predictive control (MPC) algorithm to optimize the HVP control. Then, a DDMPC optimal scheme is designed with a target equilibrium and a pair of initial/terminal constraints. Its recursive feasibility and exponential stability are guaranteed by an I/O-to-state stability (IOSS) Lyapunov function and an optimal sum cost function. The string stability analysis of HVP also is provided. Finally, several experiments with heterogeneous vehicular platoon demonstrate the effectiveness of the proposed DDMPC strategy.

引用

下载

页码：9969 / 9983

页数：15

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