Evidence map›Paper›PMID 36433317›Full record

ArticleSensors (Basel, Switzerland)2022

Microsatellite Uncertainty Control Using Deterministic Artificial Intelligence.

Evan Wilt, Timothy Sands

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Proposals for Surmounting Sensor Noises.Sensors (Basel, Switzerland) · 2023
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Evan WiltSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA.
Timothy SandsDepartment of Mechanical and Aerospace Engineering, Naval Postgraduate School, Monterey, CA 93943, USA.ORCID 0000-0002-4681-7919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This manuscript explores the applications of deterministic artificial intelligence (DAI) in a space environment in response to unknown sensor noise and sudden changes in craft physical parameters. The current state of the art literature has proposed the method, but only ideal environments, and accordingly this article addresses the literature gaps by critically evaluating efficacy in the face of unaddressed parametric uncertainties. We compare an idealized combined non-linear feedforward (FFD) and linearized feedback (FB) control scheme with an altered feedforward, feedback, and deterministic artificial intelligence scheme in the presence of simulated craft damage and environmental disturbances. Mean trajectory tracking error was improved over 91%, while the standard deviation was improved over 97% whilst improving (reducing) control effort by 13%.

Indexed as

Artificial IntelligenceNeural Networks, ComputerFeedbackMicrosatellite RepeatsUncertaintyadaptive controlcontrol systemsdeterministic artificial intelligencefeedbackfeedforward

Identifiers

PMID36433317
PMCPMC9699355

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.