Decentralized and distributed model predictive control of vehicle platoons

Student thesis: Master


This thesis considers decentralized control and distributed control for vehicle platoons and more generally networked systems in a chain structure by using model predictive control (MPC) algorithms. Additionally, automatic controller synthesis is also discussed when the topology of the vehicle platoon changes.

The distributed models of the vehicle platoon are coupled through the input of the preceding vehicles. In the decentralized scheme, no communication among vehicles is available and thus the coupled input is regarded as unknown disturbance. Then, two robust MPC (RMPC) algorithms, i.e. [1] [2], are used to solve the decentralized control problem, which leads to two different decentralized model predictive control (DeMPC) schemes. It is demonstrated by simulation that the decentralized control problem can be solved by both DeMPC algorithms.

n the distributed control problem, communication among vehicles becomes available and thus the two DeMPC schemes are modified to incorporate the communication, which leads to two distributed model predictive control (DMPC) schemes. In addition, the proof of recursive feasibility for the DMPC algorithms is provided. Simulation demon-strates that the distributed control problem can be solved by both DMPC algorithms.

In addition, the decentralized control is compared with the distributed control of vehicle platoons. Overall, it is shown that the each DMPC algorithm have a larger feasible region than its corresponding DeMPC. The cost is that the communication is required and the total computation time is increased.

For the automatic controller synthesis when the topology of a platoon changes, al-gorithms of controller synthesis are provided and demonstrated by simulation for the scenario where one vehicle joins a platoon.
Date of Award30 Mar 2017
Original languageEnglish
SupervisorPaul M.J. Van den Hof (Supervisor 1), Mircea Lazar (Supervisor 2) & Theo Hofman (Supervisor 2)


  • Distributed model predictive control
  • Vehicle platooning
  • Constrained control

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