A two-stage Bayesian procedure propagates credible sets from a learned Hamiltonian into control barrier functions, delivering an end-to-end safety guarantee of at least 1 minus the sum of two independent credibility budgets.
Robust control barrier functions for constrained stabiliza- tion of nonlinear systems,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Dynamic safety margins are shown to be control barrier functions for an augmented state-reference system, enabling constraint handling agnostic to relative degree and supporting multiple constraints via control sharing.
An online learning-enhanced high-order adaptive CBF with Neural ODEs maintains safety for a 38g nano quadrotor against 18km/h wind by adapting to time-varying perturbations on the fly.
citing papers explorer
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Bayesian Safety Guarantees for Port-Hamiltonian Systems with Learned Energy Functions
A two-stage Bayesian procedure propagates credible sets from a learned Hamiltonian into control barrier functions, delivering an end-to-end safety guarantee of at least 1 minus the sum of two independent credibility budgets.
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Using Dynamic Safety Margins as Control Barrier Functions
Dynamic safety margins are shown to be control barrier functions for an augmented state-reference system, enabling constraint handling agnostic to relative degree and supporting multiple constraints via control sharing.
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Online Learning-Enhanced High Order Adaptive Safety Control
An online learning-enhanced high-order adaptive CBF with Neural ODEs maintains safety for a 38g nano quadrotor against 18km/h wind by adapting to time-varying perturbations on the fly.