Model-Free Safety-Critical Control for Robotic Systems

Molnár, Tamás G.; Cosner, Ryan K.; Singletary, Andrew; Ames, Aaron D.

doi:10.1109/lra.2021.3135569

Cited by 57 publications

(25 citation statements)

References 27 publications

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“…From the results in Theorem 3.1, safety can be ensure when the convergence rate of the approximate solution u to the optimal u * is faster than the pre-defined allowable rate of change in CBF constraints. This is consistent with the conditions of safe tracking controller in [35]. The following statement presents the safety criteria for bounded c and the biased approximate solutions.…”

Section: B Safety and Robustness Analysissupporting

confidence: 81%

“…While the dynamic controller approximates the time-varying optimal solutions of problem (5) in an exponential rate, it is not sufficient to guarantee the safety of the systems yet. An illustrative experiment on how an exponentially tracking controller may break the safety constraints can be found in [35]. Besides, the ubiquitous disturbances and modeling errors require a robust algorithm.…”

Section: B Safety and Robustness Analysismentioning

confidence: 99%

“…Assumption 3.1 (Bounded Gradient of CBF [35]): The gradient of the CBF function h(x) defined in Definition 2.1 is finite such that ∀x ∈ C, ∇ x h(x) ≤ b h where b h > 0.…”

Section: B Safety and Robustness Analysismentioning

confidence: 99%

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Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

Wang¹,

wen²,

Shi³

et al. 2022

Preprint

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This paper investigates the control barrier function (CBF) based safety-critical control for continuous nonlinear control affine systems using more efficient online algorithms by the time-varying optimization method. The idea of the algorithms is that when quadratic programming (QP) or other convex optimization algorithms needed in the CBF-based method is not computation affordable, the alternative suboptimal feasible solutions can be obtained more economically. By using the barrier-based interior point method, the constrained CBF-QP problems are transformed into unconstrained ones with suboptimal solutions tracked by two continuous descent-based algorithms. Considering the lag effect of tracking and exploiting the system information, the prediction method is added to the algorithms, which achieves exponential convergence to the time-varying suboptimal solutions. The convergence and robustness of the designed methods as well as the safety criteria of the algorithms are studied theoretically. The effectiveness is illustrated by simulations on the antiswing and obstacle avoidance tasks.

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Section: B Safety and Robustness Analysissupporting

confidence: 81%

Section: B Safety and Robustness Analysismentioning

confidence: 99%

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Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

Wang¹,

wen²,

Shi³

et al. 2022

Preprint

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show abstract

“…As such, our total parameter vector θ ∈ Θ = [−1, 2] 8 . Then, our verification problem is very similar to that which we had for the robotarium -see if the quadruped coupled with the controller in [31] can keep positive this candidate barrier function h for at least K = 150 time-steps with ∆t = 0.1.…”

Section: B Hardware Verification With Limited Datamentioning

confidence: 96%

“…In this section, we will verify two hardware systems, the Robotarium, and the Unitree A1 Quadruped steered by a variant of the controller presented in [31]. The robotarium experiments will provide further numerical validation of our scenario approach to verification.…”

Section: B Hardware Verification With Limited Datamentioning

confidence: 99%

A Barrier-Based Scenario Approach to Verify Safety-Critical Systems

Akella¹,

Ames²

2022

Preprint

Self Cite

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In this letter, we detail our randomized approach to safety-critical system verification. Our method requires limited system data to make a strong verification statement. Specifically, our method first randomly samples initial conditions and parameters for a controlled, continuous-time system and records the ensuing state trajectory at discrete intervals. Then, we evaluate these states under a candidate barrier function h to determine the constraints for a randomized linear program. The solution to this program then provides either a probabilistic verification statement or a counterexample. To show the validity of our results, we verify the robotarium simulator and identify counterexamples for its hardware counterpart. We also provide numerical evidence to validate our verification statements in the same setting. Furthermore, we show that our method is systemindependent by performing the same verification method on a quadrupedal system in a multi-agent setting as well.

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Obstacle Avoidance and Communication Distance Maintenance During Cooperative Target Encircling Control of USVs Based on Control Barrier Functions

Yu,

Lu,

2024

Lecture Notes in Electrical Engineering

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Model-Free Safety-Critical Control for Robotic Systems

Cited by 57 publications

References 27 publications

Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

A Barrier-Based Scenario Approach to Verify Safety-Critical Systems

Obstacle Avoidance and Communication Distance Maintenance During Cooperative Target Encircling Control of USVs Based on Control Barrier Functions

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