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Learning for Safety-Critical Control with Control Barrier Functions.
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TLDR
A machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system, ultimately achieving safe behavior.Abstract:
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model uncertainty remains a significant challenge in synthesizing safe controllers, leading to degradation in the properties provided by the controllers. This paper develops a machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system. This approach iteratively collects data and updates a controller, ultimately achieving safe behavior. We validate this method in simulation and experimentally on a Segway platform.read more
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Differential Equations and Dynamical Systems
TL;DR: In this paper, the Third Edition of the Third edition of Linear Systems: Local Theory and Nonlinear Systems: Global Theory (LTLT) is presented, along with an extended version of the second edition.
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Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
TL;DR: In this article, a reinforcement learning framework was proposed to learn the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program.
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Robust Adaptive Control Barrier Functions: An Adaptive and Data-Driven Approach to Safety
TL;DR: 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.
Proceedings ArticleDOI
Learning Control Barrier Functions from Expert Demonstrations
Alexander Robey,Haimin Hu,Lars Lindemann,Hanwen Zhang,Dimos V. Dimarogonas,Stephen Tu,Nikolai Matni +6 more
TL;DR: In this article, a learning-based approach to safe controller synthesis based on control barrier functions (CBFs) is proposed, which is agnostic to the parameterization used to represent the CBF.
Journal ArticleDOI
Robust Safety-Critical Control for Dynamic Robotics
Quan Nguyen,Koushil Sreenath +1 more
TL;DR: A novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty is presented.
References
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