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.
A contractio n theory approach to stochastic incremental stability,
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2019 2verdicts
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Establishes Riemannian metrics that define contraction regions for primal-dual gradient dynamics, yielding convergence rates for equality-constrained and augmented-Lagrangian inequality-constrained convex problems under suitable step-size choices.
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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.
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Contraction Analysis on Primal-Dual Gradient Optimization
Establishes Riemannian metrics that define contraction regions for primal-dual gradient dynamics, yielding convergence rates for equality-constrained and augmented-Lagrangian inequality-constrained convex problems under suitable step-size choices.