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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
Citations
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Proceedings ArticleDOI
Safe Control Using High-Order Measurement Robust Control Barrier Functions
TL;DR: In this article , the authors study the problem of providing safety guarantees for dynamic systems of high relative degree in the presence of state measurement errors and propose an extension of the Measurement Robust Control Barrier Functions (HO-MR-CBFs), which can render the system's safe set forward invariant.
Journal ArticleDOI
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
TL;DR: Wang et al. as mentioned in this paper developed a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method to learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer.
Proceedings ArticleDOI
Thermal Fault-Tolerance in Lithium-ion Battery Cells: A Barrier Function based Input-To-State Safety Framework
TL;DR: In this paper , a fault-tolerant control algorithm for Li-ion batteries is proposed to guarantee both thermal safety and stability of the battery cells under thermal anomalies by combining a lumped parameter thermal model and Ordinary Differential Equation (ODE)-based practical input-to-state safety technique.
Proceedings ArticleDOI
Reinforcement Learning of Space Robotic Manipulation with Multiple Safety Constraints
TL;DR: A novel algorithm called Safe policy optimization with multiple constraints (SPOMC) is proposed, which incorporates two constraints (namely energy constraint and geometric constraint) into the constrained policy optimization framework, maximizing expected total reward while maintaining a bounded expected total cost.
Proceedings ArticleDOI
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions
TL;DR: This paper proposes a provably sample efficient episodic safe learning framework for online control tasks that leverages safe exploration and exploitation in an unknown, nonlinear dynamical system to achieve provable high-probability safety under uncertainty during model learning.
References
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Journal ArticleDOI
Reinforcement learning in robotics: A survey
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
Journal ArticleDOI
Survey paper: Set invariance in control
TL;DR: An overview of the literature concerning positively invariant sets and their application to the analysis and synthesis of control systems is provided.
Book
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.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers
Book
Nonlinear Systems: Analysis, Stability, and Control
TL;DR: In this article, the authors compare Linear vs. Nonlinear Control of Differential Geometry with Linearization by State Feedback (LSF) by using Linearization and Geometric Non-linear Control (GNC).