5,951 research outputs found
Composing Control Barrier Functions for Complex Safety Specifications
The increasing complexity of control systems necessitates control laws that
guarantee safety w.r.t. complex combinations of constraints. In this letter, we
propose a framework to describe compositional safety specifications with
control barrier functions (CBFs). The specifications are formulated as Boolean
compositions of state constraints, and we propose an algorithmic way to create
a single continuously differentiable CBF that captures these constraints and
enables safety-critical control. We describe the properties of the proposed
CBF, and we demonstrate its efficacy by numerical simulations.Comment: Submitted to the IEEE Control System Letters (L-CSS) and the 2024
American Control Conference (ACC). 6 pages, 3 figure
On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions
Connected automated vehicles have shown great potential to improve the
efficiency of transportation systems in terms of passenger comfort, fuel
economy, stability of driving behavior and mitigation of traffic congestions.
Yet, to deploy these vehicles and leverage their benefits, the underlying
algorithms must ensure their safe operation. In this paper, we address the
safety of connected cruise control strategies for longitudinal car following
using control barrier function (CBF) theory. In particular, we consider various
safety measures such as minimum distance, time headway and time to conflict,
and provide a formal analysis of these measures through the lens of CBFs.
Additionally, motivated by how stability charts facilitate stable controller
design, we derive safety charts for existing connected cruise controllers to
identify safe choices of controller parameters. Finally, we combine the
analysis of safety measures and the corresponding stability charts to
synthesize safety-critical connected cruise controllers using CBFs. We verify
our theoretical results by numerical simulations.Comment: Accepted to the 62nd IEEE Conference on Decision and Control. 6
pages, 5 figure
Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
We introduce a methodology to guarantee safety against the spread of
infectious diseases by viewing epidemiological models as control systems and by
considering human interventions (such as quarantining or social distancing) as
control input. We consider a generalized compartmental model that represents
the form of the most popular epidemiological models and we design
safety-critical controllers that formally guarantee safe evolution with respect
to keeping certain populations of interest under prescribed safe limits.
Furthermore, we discuss how measurement delays originated from incubation
period and testing delays affect safety and how delays can be compensated via
predictor feedback. We demonstrate our results by synthesizing active
intervention policies that bound the number of infections, hospitalizations and
deaths for epidemiological models capturing the spread of COVID-19 in the USA.Comment: Submitted to the IEEE Control System Letters (L-CSS) and the 2021
American Control Conference (ACC). 6 pages, 3 figure
Verifying Safe Transitions between Dynamic Motion Primitives on Legged Robots
Functional autonomous systems often realize complex tasks by utilizing state
machines comprised of discrete primitive behaviors and transitions between
these behaviors. This architecture has been widely studied in the context of
quasi-static and dynamics-independent systems. However, applications of this
concept to dynamical systems are relatively sparse, despite extensive research
on individual dynamic primitive behaviors, which we refer to as "motion
primitives." This paper formalizes a process to determine dynamic-state aware
conditions for transitions between motion primitives in the context of safety.
The result is framed as a "motion primitive graph" that can be traversed by
standard graph search and planning algorithms to realize functional autonomy.
To demonstrate this framework, dynamic motion primitives -- including standing
up, walking, and jumping -- and the transitions between these behaviors are
experimentally realized on a quadrupedal robot
Safety-Critical Control of Active Interventions for COVID-19 Mitigation
The world has recently undergone the most ambitious mitigation effort in a century, consisting of wide-spread quarantines aimed at preventing the spread of COVID-19. The use of influential epidemiological models of COVID-19 helped to encourage decision makers to take drastic non-pharmaceutical interventions. Yet, inherent in these models are often assumptions that the active interventions are static, e.g., that social distancing is enforced until infections are minimized, which can lead to inaccurate predictions that are ever evolving as new data is assimilated. We present a methodology to dynamically guide the active intervention by shifting the focus from viewing epidemiological models as systems that evolve in autonomous fashion to control systems with an “input” that can be varied in time in order to change the evolution of the system. We show that a safety-critical control approach to COVID-19 mitigation gives active intervention policies that formally guarantee the safe evolution of compartmental epidemiological models. This perspective is applied to current US data on cases while taking into account reduction of mobility, and we find that it accurately describes the current trends when time delays associated with incubation and testing are incorporated. Optimal active intervention policies are synthesized to determine future mitigations necessary to bound infections, hospitalizations, and death, both at national and state levels. We therefore provide means in which to model and modulate active interventions with a view toward the phased reopenings that are currently beginning across the US and the world in a decentralized fashion. This framework can be converted into public policies, accounting for the fractured landscape of COVID-19 mitigation in a safety-critical fashion
Safety-Critical Control of Compartmental Epidemiological Models with Measurement Delays
We introduce a methodology to guarantee safety against the spread of infectious diseases by viewing epidemiological models as control systems and human interventions (such as quarantining or social distancing) as control input. We consider a generalized compartmental model that represents the form of the most popular epidemiological models and we design safety-critical controllers that formally guarantee safe evolution with respect to keeping certain populations of interest under prescribed safe limits. Furthermore, we discuss how measurement delays originated from incubation period and testing delays affect safety and how delays can be compensated via predictor feedback. We demonstrate our results by synthesizing active intervention policies that bound the number of infections, hospitalizations and deaths for epidemiological models capturing the spread of COVID-19 in the USA
Safety-Critical Control of Active Interventions for COVID-19 Mitigation
The world has recently undergone the most ambitious mitigation effort in a century, consisting of wide-spread quarantines aimed at preventing the spread of COVID-19. The use of influential epidemiological models of COVID-19 helped to encourage decision makers to take drastic non-pharmaceutical interventions. Yet, inherent in these models are often assumptions that the active interventions are static, e.g., that social distancing is enforced until infections are minimized, which can lead to inaccurate predictions that are ever evolving as new data is assimilated. We present a methodology to dynamically guide the active intervention by shifting the focus from viewing epidemiological models as systems that evolve in autonomous fashion to control systems with an “input” that can be varied in time in order to change the evolution of the system. We show that a safety-critical control approach to COVID-19 mitigation gives active intervention policies that formally guarantee the safe evolution of compartmental epidemiological models. This perspective is applied to current US data on cases while taking into account reduction of mobility, and we find that it accurately describes the current trends when time delays associated with incubation and testing are incorporated. Optimal active intervention policies are synthesized to determine future mitigations necessary to bound infections, hospitalizations, and death, both at national and state levels. We therefore provide means in which to model and modulate active interventions with a view toward the phased reopenings that are currently beginning across the US and the world in a decentralized fashion. This framework can be converted into public policies, accounting for the fractured landscape of COVID-19 mitigation in a safety-critical fashion
Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions
This work provides formal safety guarantees for control systems with
disturbance. A disturbance observer-based robust safety-critical controller is
proposed, that estimates the effect of the disturbance on safety and utilizes
this estimate with control barrier functions to attain provably safe dynamic
behavior. The observer error bound - which consists of transient and
steady-state parts - is quantified, and the system is endowed with robustness
against this error via the proposed controller. An adaptive cruise control
problem is used as illustrative example through simulations including real
disturbance data.Comment: 6 pages, 5 figure
First Astronomical Use of Multiplexed Transition Edge Bolometers
We present performance results based on the first astronomical use of multiplexed superconducting bolometers. The Fabry-Perot Interferometer Bolometer Research Experiment
(FIBRE) is a broadband submillimeter spectrometer that achieved first light in June 2001 at the Caltech Submillimeter Observatory (CSO). FIBRE'S detectors are superconducting transition edge sensor (TES) bolometers read out by a SQUID multiplexer. The Fabry-Perot uses a low
resolution grating to order sort the incoming light. A linear bolometer array consisting of 16 elements detects this dispersed light, capturing 5 orders simultaneously from one position on the sky. With tuning of the Fabry-Perot over one free spectral range, a spectrum covering Δλ/λ= 1/7 at a resolution of δλ/λ ≈ 1/1200 can be acquired. This spectral resolution is sufficient to resolve
Doppler-broadened line emission from external galaxies. FIBRE operates in the 350 µm and 450 µm bands. These bands cover line emission from the important star formation tracers neutral
carbon [Cl] and carbon monoxide (CO). We have verified that the multiplexed bolometers are
photon noise limited even with the low power present in moderate resolution spectrometry
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