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Control Contraction Metrics on Finsler Manifolds
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Control Contraction Metrics (CCMs) provide a nonlinear controller design involving an offline search for a Riemannian metric and an online search for a shortest path between the current and desired trajectories. In this paper, we generalize CCMs to Finsler geometry, allowing the use of non-Riemannian metrics. We provide open loop and sampled data controllers. The sampled data control construction presented here does not require real time computation of globally shortest paths, simplifying computation.
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Cited by 2 Pith papers
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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 ...
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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 und...
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