This paper presents a novel distributed model predictive control (MPC)
formulation without terminal cost and a corresponding distributed synthesis
approach for distributed linear discrete-time systems with coupled constraints.
The proposed control scheme introduces an explicit stability condition as an
additional constraint based on relaxed dynamic programming. Par conséquent,
contrary to other related approaches, system stability with the developed
controller does not rely on designing a terminal cost. A distributed synthesis
approach is then introduced to handle the stability constraint locally within
each local agent. To solve the underlying optimization problem for distributed
MPC, a violation-free distributed optimization approach is developed, using
constraint tightening to ensure feasibility throughout iterations. A numerical
example demonstrates that the proposed distributed MPC approach ensures
closed-loop stability for each feasible control sequence, with each agent
computing its control input in parallel.

Cet article explore les excursions dans le temps et leurs implications.

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