290 citations · 1.5k across the 41 of their papers we have counts for
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A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine +1
This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov fun…
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniforml…
Meta-Adaptive Nonlinear Control: Theory and Algorithms
Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell +2
We present an online multi-task learning approach for adaptive nonlinear control, which we call Online Meta-Adaptive Control (OMAC). The goal is to control a nonlinear system subje…
Learning-based Adaptive Control using Contraction Theory
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine
Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance. This paper thus presents a deep learning-based adaptive con…
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems. It us…
Online Optimization with Memory and Competitive Control
Guanya Shi, Yiheng Lin, Soon-Jo Chung +2
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hit…