30 citations · 31 across the 2 of their papers we have counts for
6 papers
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…
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…
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach
Hiroyasu Tsukamoto, Soon-Jo Chung
This paper presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with form…
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…
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
Hiroyasu Tsukamoto, Soon-Jo Chung
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long…
Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization
Hiroyasu Tsukamoto, Soon-Jo Chung
This paper presents ConVex optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM), a new state feedback control framework for a class of Ito stochastic no…