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Found 10 papers, 3 papers with code
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy.
Interstellar Object Accessibility and Mission Design
Interstellar objects (ISOs) are fascinating and under-explored celestial objects, providing physical laboratories to understand the formation of our solar system and probe the composition and properties of material formed in exoplanetary systems.
Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees.
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
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 function, the existence of which is a necessary and sufficient condition for incremental exponential stability of non-autonomous nonlinear system trajectories.
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
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 uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other.
Learning-based Adaptive Control using Contraction Theory
Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance.
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach
This paper presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory.
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
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.
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM).
Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization
For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases.
Systems and Control Robotics Systems and Control
