Low-Latency Robust MPC for CACC under Variable Road Geometry⁎This work was supported in part by the Engineering and Physical Sciences Research Council of UK under the EPSRC Innovation Fellowship scheme (EP/S001956/1) and in part by the Royal Society Newton Advanced Fellowship scheme (NAF\R1\201213). Issue 2 (2020)

• Record Type: Journal Article
• Authors: Lan, Jianglin; Zhao, Dezong; Tian, Daxin
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 15116
• Page End: 15121
• Publication Date: 2020
• Subjects: Cooperative adaptive cruise control -- lane keeping -- predictive control -- road geometry -- time delay; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2035
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Cooperative adaptive cruise control (CACC) has attracted much research attention, due to its great potential in improving traffic throughput, safety and energy efficiency. This paper aims to address the following problems that are rarely investigated in the literature: (i) the time delay caused by online computation of the optimal control action, and (ii) robustly stable CACC under variable road geometry. To this end, a one-step ahead robust model predictive control (MPC) is developed for achieving CACC and lane keeping (LK) of the followers in the platoon, by leveraging vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In the proposed design, the current MPC policy is generated one-step ahead during the previous sampling period to avoids the optimization-induced time delay existing in the traditional MPC. LK control is incorporated with CACC to ensure vehicle lateral stability and vehicle following under variable road geometry. The MPC design is formulated as an easily solved linear matrix inequality (LMI) optimization problem with consideration of the control input limits and constraints on platooning errors and lateral displacement. Effectiveness of the proposed MPC and its advantages over the traditional MPC are verified by simulating a vehicle platoon on roads with time-varying bank, curvature and grade.