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. As a result,
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.

Questo articolo esplora i giri e le loro implicazioni.

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