Contraction Theory for Machine Learning

A Tutorial Overview

How can we mathematically ensure the safety, stability, and robustness of machine learning-based control and estimation systems?

This website provides a tutorial overview of contraction theory for nonlinear stability analysis and control synthesis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based control problems.

Contraction Theory Tutorial Papers for Beginners

The original paper that derives contraction theory for nonlinear incremental stability analysis

A tutorial paper on utilizing contraction theory for learning-based control with formal robustness and stability guarantees

Tutorial Session at 60th IEEE Conference on Decision and Control (CDC)

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