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Thesis_Final_Document_Peng_Liu.pdf (11.76 MB)

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Distributed Model Predictive Control for Cooperative Highway Driving

Liu, Peng
http://orcid.org/0000-0002-4979-4708
http://rave.ohiolink.edu/etdc/view?acc_num=osu1500564857136091

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Cooperative highway driving systems (CHDSs) consist of collaborating vehicles with automated control units and vehicle-to-vehicle communication capabilities. Such systems are proposed as an important component of intelligent transportation systems (ITS) aiming at improving energy efficiency and driving safety. CHDSs have a broad spectrum of applications, ranging from automated freight systems to highway automation to smart city transit. Modeling and control of cooperative vehicles on highways contributes importantly to CHDS development. This problem is of critical importance in developing safe and reliable controllers and establishing frameworks and criteria verifying CHDS performance. This work focuses on the cooperative control problems in developing CHDSs by investigating distributed model predictive control (DMPC) techniques. In particular, collaboration of connected and automated vehicles is first formulated into a constrained optimization problem. Then, different DMPC strategies are investigated considering features of the cooperative control problem in a CHDS. We focus on non-iterative DMPC schemes with partially parallel information exchange between subsystems. Feasibility and stability properties of the closed-loop system applying non-iterative DMPC are established taking into account the coupling of the control input with state predictions calculated at previous step. Furthermore, a non-iterative DMPC scheme implementing a partitioning procedure is proposed to reduce the conservatism of compatibility constraints while guaranteeing safe inter-vehicle distances. With the DMPC scheme controlling the connected and automated vehicles, we further investigate interactions of cooperative driving groups with surrounding human-operated vehicles in mixed traffic environments. A behavior classification framework is developed to detect driver behaviors of surrounding human-operated vehicles. With the behavior classification framework, a behavior-guided MPC controller is proposed to address disturbances caused by human-operated vehicles. Finally, the potential benefits of implementing cooperative highway driving systems is verified using microscopic traffic simulation.
Umit Ozguner (Advisor)
170 p.
Electrical Engineering; Robotics; Transportation
cooperative driving; connected and automated vehicles; CAV; highway collaboration; distributed model predictive control; spatially interconnected systems; DMPC

Recommended Citations

Citations

  • Liu, P. (2017). Distributed Model Predictive Control for Cooperative Highway Driving [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500564857136091

    APA Style (7th edition)

  • Liu, Peng. Distributed Model Predictive Control for Cooperative Highway Driving. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1500564857136091.

    MLA Style (8th edition)

  • Liu, Peng. "Distributed Model Predictive Control for Cooperative Highway Driving." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500564857136091

    Chicago Manual of Style (17th edition)

osu1500564857136091
442
© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.