Input-to-State Safety With Control Barrier Functions

Kolathaya, Shishir; Ames, Aaron D.

doi:10.1109/lcsys.2018.2853698

Cited by 201 publications

(142 citation statements)

References 19 publications

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“…Controllers synthesized via CBFs rely on a model, and the guarantees they achieve may fail in the presence of model uncertainty. Robust control methods can ensure safety [7], [28] or quantify how safety properties degrade [9] in the presence of model uncertainty, but may be overly conservative in restricting the behavior of the system. Data-driven methods employing machine learning [19], [5] provide probabilistic safety guarantees, but may require episodic, offline training to improve model estimates [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Safety with Control Barrier Functions

Taylor

Ames

2020

2020 American Control Conference (ACC)

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153

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Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunovbased methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the systems state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an adaptive cruise control (ACC) simulation.

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Section: Introductionmentioning

confidence: 99%

Adaptive Safety with Control Barrier Functions

Taylor

Ames

2020

2020 American Control Conference (ACC)

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153

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“…Control barrier functions (CBFs) were used to provide guarantees on the safety of continuous-time nonlinear systems with affine inputs for an adaptive cruise control application in [6]. The notion of input-to-state CBFs that ensured the safety of nonlinear systems under arbitrary input disturbances was introduced in [24], and safety was characterized in terms of the invariance of a set whose computation depended on the magnitude of the disturbance. The authors of [45] relaxed the supermartingale condition that a barrier certificate had to satisfy in [36] in order to provide finite-time guarantees on the safety of a system.…”

Section: Related Workmentioning

confidence: 99%

Linear Temporal Logic Satisfaction in Adversarial Environments Using Secure Control Barrier Certificates

Ramasubramanian

Niu

Clark

et al. 2019

Lecture Notes in Computer Science

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This paper studies the satisfaction of a class of temporal properties for cyber-physical systems (CPSs) over a finite-time horizon in the presence of an adversary, in an environment described by discrete-time dynamics. The temporal logic specification is given in sa f e − LTL F , a fragment of linear temporal logic over traces of finite length. The interaction of the CPS with the adversary is modeled as a two-player zero-sum discretetime dynamic stochastic game with the CPS as defender. We formulate a dynamic programming based approach to determine a stationary defender policy that maximizes the probability of satisfaction of a sa f e − LTL F formula over a finite time-horizon under any stationary adversary policy. We introduce secure control barrier certificates (S-CBCs), a generalization of barrier certificates and control barrier certificates that accounts for the presence of an adversary, and use S-CBCs to provide a lower bound on the above satisfaction probability. When the dynamics of the evolution of the system state has a specific underlying structure, we present a way to determine an S-CBC as a polynomial in the state variables using sum-of-squares optimization. An illustrative example demonstrates our approach.

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“…Safety guarantees endowed by a controller synthesized via CBFs rely on an accurate model of a system's dynamics, and may degrade in the presence of model uncertainty. The recently proposed definition of Input-to-State Safety (ISSf) provides a tool for quantifying the impact on safety guarantees of such uncertainty or disturbances in the dynamics [13] by describing changes in the set kept invariant.…”

Section: Introductionmentioning

confidence: 99%

A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety

Taylor

Singletary

Yue

et al. 2021

IEEE Control Syst. Lett.

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In this paper we seek to quantify the ability of learning to improve safety guarantees endowed by Control Barrier Functions (CBFs). In particular, we investigate how model uncertainty in the time derivative of a CBF can be reduced via learning, and how this leads to stronger statements on the safe behavior of a system. To this end, we build upon the idea of Inputto-State Safety (ISSf) to define Projection-to-State Safety (PSSf), which characterizes degradation in safety in terms of a projected disturbance. This enables the direct quantification of both how learning can improve safety guarantees, and how bounds on learning error translate to bounds on degradation in safety. We demonstrate that a practical episodic learning approach can use PSSf to reduce uncertainty and improve safety guarantees in simulation and experimentally.

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Input-to-State Safety With Control Barrier Functions

Cited by 201 publications

References 19 publications

Adaptive Safety with Control Barrier Functions

Adaptive Safety with Control Barrier Functions

Linear Temporal Logic Satisfaction in Adversarial Environments Using Secure Control Barrier Certificates

A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety

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