A DMPC method is proposed that solves the dual problem via inexact primal-dual gradient optimization with Laplacian consensus and uses contraction theory to guarantee convergence, recursive feasibility, and stability under premature termination.
Robust self-triggered mi n–max model predictive control for discrete-time nonlinear systems
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Distributed Model Predictive Control Under Inexact Primal-Dual Gradient Optimization Based on Contraction Analysis
A DMPC method is proposed that solves the dual problem via inexact primal-dual gradient optimization with Laplacian consensus and uses contraction theory to guarantee convergence, recursive feasibility, and stability under premature termination.