A two-time-scale dynamic implementation enables locally computable approximations of networked control barrier function safety filters with explicit bounds on trajectory mismatch and safety degradation.
Explicit Control Barrier Function-based Safety Filters and their Resource-Aware Computation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
This paper studies the efficient implementation of safety filters that are designed using control barrier functions (CBFs), which minimally modify a nominal controller to render it safe with respect to a prescribed set of states. Although CBF-based safety filters are often implemented by solving a quadratic program (QP) in real time, the use of off-the-shelf solvers for such optimization problems poses a challenge in applications where control actions need to be computed efficiently at very high frequencies. In this paper, we introduce a closed-form expression for controllers obtained through CBF-based safety filters. This expression is obtained by partitioning the state-space into different regions, with a different closed-form solution in each region. We leverage this formula to introduce a resource-aware implementation of CBF-based safety filters that detects changes in the partition region and uses the closed-form expression between changes. We showcase the applicability of our approach in examples ranging from aerospace control to safe reinforcement learning.
representative citing papers
Learned Lyapunov functions, residual SAC policies, and PINNs are combined with a Slotine-Li controller and a closed-form safety filter to improve tracking on uncertain Euler-Lagrange systems while retaining stability guarantees.
A convex-minimization controller for affine inequalities is approximated by neural networks that act as universal formulas independent of state dimension for bounded input and constraint sizes.
citing papers explorer
-
Local Safety Filters for Networked Systems via Two-Time-Scale Design
A two-time-scale dynamic implementation enables locally computable approximations of networked control barrier function safety filters with explicit bounds on trajectory mismatch and safety degradation.
-
Learned Lyapunov Shielding for Adaptive Control
Learned Lyapunov functions, residual SAC policies, and PINNs are combined with a Slotine-Li controller and a closed-form safety filter to improve tracking on uncertain Euler-Lagrange systems while retaining stability guarantees.
-
Universal Formulas for Safe Control and Their Neural Network Approximations
A convex-minimization controller for affine inequalities is approximated by neural networks that act as universal formulas independent of state dimension for bounded input and constraint sizes.