Journal ArticleDOI
Robust Adaptive Control Barrier Functions: An Adaptive and Data-Driven Approach to Safety
Brett T. Lopez,Jean-Jacques E. Slotine,Jonathan P. How +2 more
- Vol. 5, Iss: 3, pp 1031-1036
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TLDR
A new framework is developed for control of constrained nonlinear systems with structured parametric uncertainty and forward invariance of a safe set is achieved through online parameter adaptation and data-driven model estimation.Abstract:
A new framework is developed for control of constrained nonlinear systems with structured parametric uncertainty. Forward invariance of a safe set is achieved through online parameter adaptation and data-driven model estimation. The new adaptive data-driven safety paradigm is merged with a recent adaptive controller for systems nominally contracting in closed-loop. This unification is more general than other safety controllers as contraction does not require the system be invertible or in a particular form. The method is tested on the pitch dynamics of an aircraft with uncertain nonlinear aerodynamics.read more
Citations
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Aerodynamics Aeronautics And Flight Mechanics
TL;DR: The aerodynamics aeronautics and flight mechanics is universally compatible with any devices to read and an online access to it is set as public so you can get it instantly.
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Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke,Melissa Greeff,Adam W. Hall,Zhaocong Yuan,Siqi Zhou,Jacopo Panerati,Angela P. Schoellig +6 more
TL;DR: A review of the recent advances made in using machine learning to achieve safe decision making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research can be found in this article.
Journal ArticleDOI
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
TL;DR: In this paper , a review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research, is presented.
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Learning Stability Certificates from Data
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Proceedings ArticleDOI
Adaptive Robust Quadratic Programs using Control Lyapunov and Barrier Functions
TL;DR: In this article, an adaptive robust quadratic program (QP) based control using control Lyapunov and barrier functions for nonlinear systems subject to time-varying and state-dependent uncertainties is presented.
References
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Journal ArticleDOI
On contraction analysis for non-linear systems
TL;DR: These results may be viewed as generalizing the classical Krasovskii theorem, and, more loosely, linear eigenvalue analysis, and the approach is illustrated by controller and observer designs for simple physical examples.
Nonlinear And Adaptive Control Design
TL;DR: Nonlinear Control Design: Geometric, Adaptive and Robust ... 1.2.3 Adaptive control as dynamic nonlinear feedback 1.1.4 Lyapunov-based design Adaptive Nonlinear Control.
Journal ArticleDOI
Control Barrier Function Based Quadratic Programs for Safety Critical Systems
TL;DR: This paper develops a methodology that allows safety conditions—expression as control barrier functions—to be unified with performance objectives—expressed as control Lyapunov functions—in the context of real-time optimization-based controllers.
Book
Aerodynamics, Aeronautics, and Flight Mechanics
TL;DR: In this paper, the authors discuss the production of Thrust Airplane Performance Helicopters and V/STOL Aircraft Static Stability and Control Open-Loop DSC Controlled Motion and Automatic Stability.
Proceedings ArticleDOI
Control Barrier Functions: Theory and Applications
Aaron D. Ames,Samuel Coogan,Magnus Egerstedt,Gennaro Notomista,Koushil Sreenath,Paulo Tabuada +5 more
TL;DR: In this paper, the authors provide an introduction and overview of control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers.