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CAV platooning technology has received considerable attention in the past fewyears, driven by the next generation smart transportation systems. Unlike mostof the existing platooning methods that focus on linear vehicle dynamics ofCAVs, this paper considers nonlinear vehicle dynamics and develops fullydistributed optimization based CAV platooning control schemes via the modelpredictive control (MPC) approach for a possibly heterogeneous CAV platoon. Thenonlinear vehicle dynamics leads to several major difficulties in distributedalgorithm development and control analysis and design. Specifically, theunderlying MPC optimization problem is nonconvex and densely coupled. Further,the closed loop dynamics becomes a time-varying nonlinear system subject toexternal perturbations, making closed loop stability analysis rathercomplicated. To overcome these difficulties, we formulate the underlying MPCoptimization problem as a locally coupled, albeit nonconvex, optimizationproblem and develop a sequential convex programming based fully distributedscheme for a general MPC horizon. Such a scheme can be effectively implementedfor real-time computing using operator splitting methods. To analyze the closedloop stability, we apply various tools from global implicit function theorems,stability of linear time-varying systems, and Lyapunov theory forinput-to-state stability to show that the closed loop system is locallyinput-to-state stable uniformly in all small coefficients pertaining to thenonlinear dynamics. Numerical tests on homogeneous and heterogeneous CAVplatoons demonstrate the effectiveness of the proposed fully distributedschemes and CAV platooning control.

Nonconvex, Fully Distributed Optimization based CAV Platooning Control under Nonlinear Vehicle Dynamics