Nonconvex, Fully Distributed Optimization Based CAV Platooning Control Under Nonlinear Vehicle Dynamics

CAV platooning technology has received considerable attention, driven by the next generation smart transportation systems. This paper considers nonlinear vehicle dynamics and develops fully distributed optimization based CAV platooning control schemes via the platoon centered MPC approach for a poss...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 23; no. 11; pp. 20506 - 20521
Main Authors: Shen, Jinglai, Kammara, Eswar Kumar Hathibelagal, Du, Lili
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerodynamics
Algorithms
car following control
Closed loops
Computational geometry
Connected and autonomous vehicle
Convexity
distributed algorithm
Dynamical systems
Feedback control
input-to-state stability
Lyapunov stability theory
Mathematical programming
nonconvex optimization
Nonlinear control
Nonlinear dynamical systems
Nonlinear dynamics
Nonlinear systems
Numerical stability
Optimization
Perturbation
Platooning
Stability analysis
Stability criteria
Time varying control systems
Time-varying systems
Traffic
Transportation systems
Vehicle dynamics
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:CAV platooning technology has received considerable attention, driven by the next generation smart transportation systems. This paper considers nonlinear vehicle dynamics and develops fully distributed optimization based CAV platooning control schemes via the platoon centered MPC approach for a possibly heterogeneous CAV platoon. The nonlinear vehicle dynamics leads to major difficulties in distributed algorithm development and control analysis. Specifically, the underlying MPC optimization problem is nonconvex and densely coupled. Further, the closed loop dynamics becomes a time-varying nonlinear system with non-vanishing external perturbations, making stability analysis rather complicated. To overcome these difficulties, we formulate the underlying MPC optimization problem as a locally coupled, albeit nonconvex, optimization problem and develop a sequential convex programming based fully distributed scheme for a general MPC horizon. Such a scheme can be effectively implemented for real-time computing using operator splitting methods. To analyze the closed loop stability, we apply various tools from global implicit function theorems, stability of linear time-varying systems, and Lyapunov theory for input-to-state stability to show that the closed loop system is locally input-to-state stable uniformly in all small coefficients pertaining to the nonlinear dynamic effects. Numerical tests on a heterogeneous CAV platoon in a real traffic condition illustrate the effectiveness of the proposed method.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3175668