  This paper presents a distributed model predictive control (DMPC) algorithm
for heterogeneous vehicle platoons with unidirectional topologies and a priori
unknown desired set point. The vehicles (or nodes) in a platoon are dynamically
decoupled but constrained by spatial geometry. Each node is assigned a local
open-loop optimal control problem only relying on the information of
neighboring nodes, in which the cost function is designed by penalizing on the
errors between predicted and assumed trajectories. Together with this
penalization, an equality based terminal constraint is proposed to ensure
stability, which enforces the terminal states of each node in the predictive
horizon equal to the average of its neighboring states. By using the sum of
local cost functions as a Lyapunov candidate, it is proved that asymptotic
stability of such a DMPC can be achieved through an explicit sufficient
condition on the weights of the cost functions. Simulations with passenger cars
demonstrate the effectiveness of proposed DMPC.
