Tube-based Distributed Model Predictive Control for Heterogeneous Vehicle Platoons via Convex Optimization

Connected and autonomous driving technology is a current research hotspot, which can effectively ease transportation congestion and enhance road safety. This paper addresses the control problem of heterogeneous vehicle platoons subject to various disturbances, including but not limited to modeling and measurement disturbances. The problem is approached by a tube-based distributed model predictive control (MPC) algorithm that relies on a unidirectional predecessor-following communication topology. To improve the computational efficiency of the algorithm, the inherently nonlinear MPC problem is formulated as a convex program in the spatial domain by coordination changes and suitably relaxing the nonconvex constraints. Numerical simulation examples are carried out to show the effectiveness and robustness of the proposed control algorithm.