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Immersion and Invariance-Based Tracking Control for Quadrotor-Slung-Payload Systems

2025· article· W7123354573 on OpenAlexaff
Junjie Kang, Yuxia Yuan, Jinjun Shan, Markus Ryll

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsYork University
Fundersnot available
KeywordsControl theory (sociology)EstimatorExponential stabilityTrajectoryPayload (computing)Convergence (economics)Position (finance)Tracking (education)Controller (irrigation)

Abstract

fetched live from OpenAlex

This paper studies the trajectory tracking control problem of a quadrotor-slung-payload system with an unknown payload mass. To achieve accurate payload mass estimation, a novel estimator is designed using the immersion and invariance manifold design technique, enabling exponential convergence for the estimated parameter error dynamics. The estimator is then integrated into a cascaded control framework, and the asymptotic stability of the position tracking closed-loop system under the estimator-based controller is theoretically proved. Numerical simulations are conducted to evaluate the effectiveness and performance of the proposed estimator, demonstrating fast and accurate parameter estimation as well as satisfactory trajectory tracking.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.248
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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