Guaranteed Vehicle Safety Control Using Control-Dependent Barrier Functions

Huang, Yiwen; Yong, Sze Zheng; Chen, Yan

doi:10.23919/acc.2019.8814761

Cited by 14 publications

(25 citation statements)

References 20 publications

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“…This way, by Theorem 1, h x (x, u) ≥ 0 for all times, which, by (9), is equivalent to ḣx (x, u) + γ(h x (x)) ≥ 0 for all times. The repeated application of Theorem 1 shows that h x (x) ≥ 0, i.e.…”

Section: Ensuring Safetymentioning

confidence: 91%

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A Safety and Passivity Filter for Robot Teleoperation Systems

Notomista

Cai

2021

Preprint

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In this paper, we present a way of enforcing safety and passivity properties of robot teleoperation systems, where a human operator interacts with a dynamical system modeling the robot. The approach does so in a holistic fashion, by combining safety and passivity constraints in a single optimization-based controller which effectively filters the desired control input before supplying it to the system. The result is a safety and passivity filter implemented as a convex quadratic program which can be solved efficiently and employed in an online fashion in many robotic teleoperation applications. Simulation results show the benefits of the approach developed in this paper applied to the human teleoperation of a second-order dynamical system.

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Section: Ensuring Safetymentioning

confidence: 91%

“…However, the condition of passivity, recalled in 1, involves the input u. Recently introduced integral CBFs (I-CBFs) [1]-which generalize control dependent CBFs [9]-can be leveraged to enforce passivity conditions. In order to take advantage of I-CBFs, the system (1) needs to be dynamically extended as follows:…”

Section: Introductionmentioning

confidence: 99%

A Safety and Passivity Filter for Robot Teleoperation Systems

Notomista

Cai

2021

Preprint

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“…As will be shown in the next section, affine input constraints are desirable in order to synthesize robot controllers under real-time constraints at high frequencies commonly used in the control loops of modern robotic platforms. To circumvent this issue, control dependent control barrier functions [23] or the more general formulation of integral control barrier functions [24], defined below, can be employed.…”

Section: B Integral Control Barrier Functionsmentioning

confidence: 99%

Online Robot Trajectory Optimization for Persistent Environmental Monitoring

Notomista¹,

Pacchierotti

Giordano

2022

IEEE Control Syst. Lett.

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This paper presents a control-theoretic approach for trajectory optimization of mobile robots suitable for environmental monitoring. The method is based on the optimization of the Constructability Gramian in order to maximize the information collected while traversing a state trajectory. The maximization of the collected information is combined with energy constraints to define an optimization-based controller that achieves persistent environmental monitoring. The results of its application to the estimation of the concentration of a diffusing gas using a mobile robot are shown in simulation.

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“…ensures the safety of the closed-loop system while modifying the control law defined in (2) in a minimally invasive fashion. Finally, note that if the dynamics of the plant are controlaffine, namely f (x, u) = f 0 (x) + f 1 (x)u for functions f 0 : R n → R n and f 1 : R n → R n×m , then u(t), defined by (7), can be computed by a quadratic program (QP). In particular, we obtain an example of the feedback controller u = k s (x) that "filters" the controller in (2) and renders the system safe:…”

Section: A Control Barrier Functionsmentioning

confidence: 99%

“…As a result, I-CBFs are defined on both the state and input, allowing for the inclusion of the input in the safety conditions encoded by this function. A related formulation is presented in [7], where control-dependent CBFs are defined: these, unlike I-CBFs, are considered for inputs with bounded time-derivative and, importantly, the integration with nominal tracking controllers was not considered. In this paper, given CBFs and I-CBFs, we present a resulting controller that guarantees safety in state and input, while minimally modifying a nominal dynamically defined controller.…”

Section: Introductionmentioning

confidence: 99%

Integral Control Barrier Functions for Dynamically Defined Control Laws

Ames

Notomista

Wardi

et al. 2021

IEEE Control Syst. Lett.

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This paper introduces integral control barrier functions (I-CBFs) as a means to enable the safety-critical integral control of nonlinear systems. Importantly, I-CBFs allow for the holistic encoding of both state constraints and input bounds in a single framework. We demonstrate this by applying them to a dynamically defined tracking controller, thereby enforcing safety in state and input through a minimally invasive I-CBF controller framed as a quadratic program.

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Guaranteed Vehicle Safety Control Using Control-Dependent Barrier Functions

Cited by 14 publications

References 20 publications

A Safety and Passivity Filter for Robot Teleoperation Systems

A Safety and Passivity Filter for Robot Teleoperation Systems

Online Robot Trajectory Optimization for Persistent Environmental Monitoring

Integral Control Barrier Functions for Dynamically Defined Control Laws

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