Safety-Critical Control for Systems with Sporadic Measurements and Dwell Time Constraints

Core Concepts

The authors extend control barrier function theory to systems with sporadic measurements and dwell time constraints, addressing impulsive and continuous actuators in a hybrid dynamical system.

Abstract

This paper introduces safety filters for systems with infrequent measurements and both continuous and impulsive actuators. It extends prior work to ensure set invariance for perturbed systems with bounded disturbances. The study is motivated by satellite control challenges, focusing on impulsive actuation models and observer designs. The research aims to guarantee satisfaction of state constraints through innovative control strategies. The content discusses the modeling of hybrid dynamical systems, including impulsive control explanation, hybrid dynamical models, measurement considerations, open-loop observers, prediction functions, and robust safety conditions. It presents simulation case studies involving satellite rendezvous in elliptical orbits and autonomous orbit stationkeeping. The study emphasizes the importance of measurement-robust control barrier functions for systems running open-loop between measurements. Key metrics include Lipschitz constants, global stability properties, forward invariance conditions, optimization-based control laws, and simulation results for different scenarios. The paper provides insights into the application of advanced control theories to address safety-critical challenges in aerospace engineering.

Stats

ℓf,r = 0.000921 ℓf,v = 0 wc = 9.2(10)^-6 m/s^2 wg(λ) = 0.05λ

Quotes

"Satellites may run long periods open-loop before receiving corrected state information." "Measurement delays are crucial due to satellites' incapability of measuring their state without external equipment." "No work has considered systems that run open-loop for long durations between measurements using CBFs."

Key Insights Distilled From

Robust Safety-Critical Control for Systems with Sporadic Measurements and Dwell Time Constraints

by Joseph Breed... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03663.pdf

Deeper Inquiries

How can the findings of this research be applied to other industries beyond aerospace engineering

The findings of this research in robust safety-critical control using Control Barrier Functions (CBFs) can be applied to various industries beyond aerospace engineering. One potential application is in autonomous vehicles, where CBFs can help ensure safe and reliable operation by enforcing constraints on the vehicle's behavior. In robotics, CBFs can be utilized to guarantee safety during human-robot interactions or in industrial automation processes. Additionally, the principles of CBF theory can be extended to healthcare systems for patient monitoring and medical device control to maintain safety protocols. The concepts developed in this research have broad implications for any system that requires robust safety-critical control with sporadic measurements.

What are potential counterarguments against using CBFs for systems with sporadic measurements

Potential counterarguments against using CBFs for systems with sporadic measurements may include concerns about computational complexity and real-time implementation challenges. Since CBFs rely on optimization-based control laws, there could be issues related to the computational resources required to solve these optimizations quickly enough for dynamic systems with limited measurement data. Another counterargument might focus on the assumptions made within the model, such as uncertainties or disturbances not being accurately represented, leading to conservative solutions that limit system performance or efficiency. Additionally, critics may argue that implementing complex barrier functions could introduce additional points of failure or vulnerabilities into a system if not carefully designed and validated.

How can the concept of minimum dwell time be adapted to different types of dynamic systems

The concept of minimum dwell time can be adapted to different types of dynamic systems by considering specific characteristics and requirements unique to each system. For continuous-time dynamical systems like chemical processes or power grids, minimum dwell time constraints could regulate how often certain actions are taken based on process dynamics or equipment limitations. In robotic applications involving motion planning algorithms, adapting minimum dwell time ensures sufficient time between consecutive movements while accounting for sensor delays or actuator response times. Furthermore, in communication networks handling data packets transmission scheduling based on minimum dwell time considerations helps optimize network performance while maintaining reliability and latency requirements.