Evidence map›Paper›PMID 38202876›Full record

ArticleSensors (Basel, Switzerland)2023

Bilinear Interpolation of Three-Dimensional Gain-Scheduled Autopilots.

Sung Mo Koo, Timothy Sands

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sung Mo KooSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA.ORCID 0000-0001-7185-2639
Timothy SandsDepartment of Mechanical Engineering (SCPD), Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-4681-7919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gain-scheduled autopilots have emerged as a dominant strategy to achieve adaptive control of coupled, non-linear engineering complexities, owing to an ability to adapt to changing operational conditions and uncertainties. This study focuses on utilizing bilinear interpolation of gain-scheduled autopilots, emphasizing enhanced system performance and robustness. Through a comprehensive investigation and comparative analysis using three disparate cases, advantages over conventional methods are revealed. Strengths and weaknesses of both simple and specialized variants (such as linear, and real-time gain-scheduling) are introduced. Three missile guidance case-studies utilize simulation time and miss distance figures of merit. Comparing the performance of bilinear interpolation and automatic instantiations to index-search, over comparable traveled distances, missile miss distances were improved 179% and 196% respectively with slightly improved computational burden.

Indexed as

bilinear interpolationcontrol and guidancecontrol gainsgain-scheduled autopilotsnonlinear controlthree-dimensional lookup table (3D-LUT)

Identifiers

PMID38202876
PMCPMC10780514

What OpenQuestion holds

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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.