Control barrier function based quadratic programs with application to adaptive cruise control

By Aaron D. Ames, Jessy W. Grizzle, Paulo Tabuada. IEEE Conference on Decision and Control, 2014

1,059 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/14e3102e6d9cd166f3a7eac1acf1eb36ed4c961b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Control and Systems Engineering

Citations in the corpus

Exponential Control Barrier Functions for enforcing high relative-degree safety-critical constraints (2016, methodology): “Then a function B : C → R is a Control Barrier Function (CBF) [1] if there exists class K functions α1 and α2 such that, for all x ∈ Int(C) = {x ∈ R : h(x) > 0},”
Nonsmooth Barrier Functions With Applications to Multi-Robot Systems (2017, background): “such safety requirements, and, subsequently, barrier functions have been used to encode a variety of system constraints across different domains, such as adaptive cruise control [5], [6], collision avoidance for ground vehicles [7], unmanned aerial vehicles [8], and remote-access robotics testbeds [”
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems (2017, background): “This robust, worst-case analysis determines a safe region in the state space and a control policy to remain inside it; a related approach involves ensuring invariance through “barrier functions” [3, 41, 45].”
Control Barrier Function Based Quadratic Programs for Safety Critical Systems (2016, positive): “, aL = 0), and v > vl , then hF reduces to the formula given in [2].”
Stabilization with guaranteed safety using Control Lyapunov-Barrier Function (2016, background): “A recent paper on the uniting of CLF and CBF has also appeared in Ames, Grizzle, and Tabuada (2014) that uses a quadratic programming approach to combine the Lyapunov inequality and Barrier certificate inequality.”
Control Barrier Functions: Theory and Applications (2019, background): “The aforementioned methods all led to the most recent formulation of certificates of safety, termed control barrier functions, as recognition of the historical developments outlined above—these were first introduced in [20], and later refined in [21].”
Discrete Control Barrier Functions for Safety-Critical Control of Discrete Systems with Application to Bipedal Robot Navigation (2017, methodology): “Similar to the continuous-time domain [2], we chose the following Control Barrier Function,”
Input-to-State Safety With Control Barrier Functions (2018, background): “Our choice for an alternative definition is motivated by the problem definition in [1].”
Control Barrier Functions for Signal Temporal Logic Tasks (2019, background): “A time-varying candidate control barrier function implies that C(t) is non-empty for each t ∈ [t0, t1], which is the standard assumption for a time-independent function to be a candidate control barrier function [12], [13].”
The Robotarium: A remotely accessible swarm robotics research testbed (2016, methodology): “To avoid damaging the hardware, a combination of offline simulation-based verification and online collision avoidance using barrier certificates (see [14], [15]) is employed.”
Robustness of Control Barrier Functions for Safety Critical Control (2016, methodology): “In contrast, the approach of Ames et al. (2014b) was to pose a feedback design problem that mediates the safety and stabilization requirements, in the sense that safety is Preprints, 5th IFAC Conference on Analysis and Design of Hybrid Systems October 14-16, 2015.”
Control Barrier Functions for Systems with High Relative Degree (2019, methodology): “If convergence to a given state is required in addition to optimality and safety, then, as in [2], HOCBF can be combined with CLF.”
Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control (2022, background): “Since these conditions are affine in u, a common choice is a quadratic program (QP) that finds the smallest-magnitude control such that u ∈ KCLF or u ∈ KESCLF [15], for example:”