Open AccessPosted Content
Learning for Safety-Critical Control with Control Barrier Functions.
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
Safety-Critical Controller Verification via Sim2Real Gap Quantification
TL;DR: This work quantifies the inaccuracy with which a given model represents a system of interest, so that it may facilitate controller synthesis and verification and produces controllers with an arbitrarily high probability of realizing desired safe behavior on system hardware.
Journal ArticleDOI
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
Sahin Lale,Yu-Yuan Shi,Guan Nan Qu,Kamyar Azizzadenesheli,Adam Wierman,Animashree Anandkumar +5 more
TL;DR: A model-based RL framework with formal stability guarantees, Krasovskii Constrained RL (KCRL), that adopts Krasavskii’s family of Lyapunov functions as a stability constraint and derives the sample complexity upper bound for stabilization of unknown nonlinear dynamical systems via the KCRL framework.
Proceedings ArticleDOI
Neural Koopman Control Barrier Functions for Safety-Critical Control of Unknown Nonlinear Systems
TL;DR: This work utilizes Koopman operator theory (KOT) to associate the (unknown) nonlinear system with a higher dimensional bilinear system and proposes a data- driven learning framework that uses a learner and a falsifier to simultaneously learn a corresponding CBF.
Posted Content
Learning Safe Neural Network Controllers with Barrier Certificates
TL;DR: A novel approach to synthesize controllers for nonlinear continuous dynamical systems with control against safety properties based on neural networks, achieving a verification-in-the-loop synthesis.
Journal ArticleDOI
Robust Data-Driven Control Barrier Functions for Unknown Continuous Control Affine Systems
TL;DR: In this paper , robust data-driven control barrier functions (CBF-DDs) are introduced to guarantee robust safety of unknown continuous control affine systems despite worst-case realizations of generalization errors from prior data under various continuity assumptions.
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