Safety-Critical Control With Nonaffine Control Inputs Via a Relaxed Control Barrier Function for an Autonomous Vehicle

Seo, Joohwan; Baek, Eunkyu; Horowitz, Roberto; Choi, Jongeun

doi:10.1109/lra.2022.3142408

Cited by 20 publications

(9 citation statements)

References 24 publications

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“…[10], [28] introduced similar relaxation strategies for CBF constraints with the subtle difference being that instead of linear multiplicative factor to generic class-K function, they only consider linear class-K functions and treat the class-K function parameter as an optimization variable. [29] considers control non-affine dynamics and in light of the resulting loss of linearity of the CBF constraint, chooses to impose it through a penalty term in the cost function, thereby converting it into an unconstrained optimization problem.…”

Section: B Current Approaches and Limitationsmentioning

confidence: 99%

Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation

Parwana¹,

Panagou²

2023

Preprint

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Control Barrier Functions offer safety certificates by dictating controllers that enforce safety constraints. However, their response depends on the class-K function that is used to restrict the rate of change of the barrier function along the system trajectories. This paper introduces the notion of Rate Tunable Control Barrier Function (RT-CBF), which allows for online tuning of the response of CBF-based controllers. In contrast to the existing CBF approaches that use a fixed (predefined) class-K function to ensure safety, we parameterize and adapt the class-K function parameters online. Furthermore, we discuss the challenges associated with multiple barrier constraints, namely ensuring that they admit a common control input that satisfies them simultaneously for all time. In practice, RT-CBF enables designing parameter dynamics for (1) a better-performing response, where performance is defined in terms of the cost accumulated over a time horizon, or (2) a less conservative response. We propose a modelpredictive framework that computes the sensitivity of the future states with respect to the parameters and uses Sequential Quadratic Programming for deriving an online law to update the parameters in the direction of improving the performance. When prediction is not possible, we also provide point-wise sufficient conditions to be imposed on any user-given parameter dynamics so that multiple CBF constraints continue to admit common control input with time. Finally, we introduce RT-CBFs for decentralized uncooperative multi-agent systems, where a trust factor, computed based on the instantaneous ease of constraint satisfaction, is used to update parameters online for a less conservative response.

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Section: B Current Approaches and Limitationsmentioning

confidence: 99%

Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation

Parwana¹,

Panagou²

2023

Preprint

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“…In practice, a safety buffer zone can be added when designing the nominal CBF by considering the vehicle size, which is interpreted as to define the safety criterion from a conservative perspective. Furthermore, the CBF in (21) incorporates the probability property of GP regression, which is not considered in other CBF control works [9], [11].…”

Section: A Control Barrier Function With Learning Modelmentioning

confidence: 99%

“…To prevent the vehicle from rollover or collision, safety guaranteed design must be considered. Safety critical control design by the control barrier function (CBF) method is an effective approach for autonomous robots and vehicle [8], [9] and other balance robots [10], [11]. Safety critical control does not explicitly design any trajectory tracking inputs and instead, CBF is used as dynamic constraint to directly update the nominal control as a safety guaranteed certification.…”

Section: Introductionmentioning

confidence: 99%

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Learning-Based Safe Motion Control of Vehicle Ski-Stunt Maneuvers

Han¹,

Yi²

2022

Preprint

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This paper presents a safety guaranteed control method for an autonomous vehicle ski-stunt maneuver, that is, a vehicle moving with two one-side wheels. To capture the vehicle dynamics precisely, a Gaussian process model is used as additional correction to the nominal model that is obtained from physical principles. We construct a probabilistic control barrier function (CBF) to guarantee the planar motion safety. The CBF and the balance equilibrium manifold are enforced as the constraints into a safety critical control form. Under the proposed control method, the vehicle avoids the obstacle collision and safely maintain the balance for autonomous skistunt maneuvers. We conduct numerical simulation validation to demonstrate the control design. Preliminary experiment results are also presented to confirm the learning-based motion control using a scaled RC truck for autonomous ski-stunt maneuvers.

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“…Control Barrier Functions (CBFs) [1] have been increasingly used to ensure the safety of various systems such as robotics [2], [3], [4], spacecraft [5], and automotive systems [6], [7]. Essentially, the CBF provides guarantees that the system's state will remain within a given safe set for all future times, given that the control input satisfies certain conditions, allowing us to design control systems that satisfy safety requirements.…”

Section: Introductionmentioning

confidence: 99%