Distributed Model Predictive Control for Heterogeneous Vehicle Platoons Under Unidirectional Topologies

By Yang Zheng, Shengbo Eben Li, Keqiang Li, Francesco Borrelli, J. Karl Hedrick. IEEE Transactions on Control Systems Technology, 2016

565 citations recorded by the source corpus.

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 the 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 the proposed DMPC.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8807eeaf5222924fcc575ec1b71497cfc1c37779

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Citations in the corpus

Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities (2017, methodology): “Here, a synthesis method of distributed MPC is presented for a heterogeneous platoon, where each vehicle is assigned a local optimal control problem only relying on its neighboring vehicles’ information [21].”
Energy saving potentials of connected and automated vehicles (2018, background): “With a queue of communicating vehicles, this becomes a distributed MPC problem [104] that is solved sequentially from the front to the back of the queue.”
Control of Connected and Automated Vehicles: State of the Art and Future Challenges (2018, background): “ MPC can incorporate nonlinear dynamics, input and state constraints, and forecasts [153, 144, 154, 145]. A distributed MPC formulation, suited for any information ﬂow topology, has been presented in [155]. Lateral vehicle control. Lateral control supervises the vehicle motion in the lateral directi”