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Record W2441432909 · doi:10.1109/amc.2016.7496329

Separated adaptive control scheme of a rotor-flying manipulator

2016· article· en· W2441432909 on OpenAlexaff
Bin Yang, Yuqing He, Jianda Han, Guangjun Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Rotor (electric)Coupling (piping)Control engineeringNonlinear systemScheme (mathematics)Mobile manipulatorMoment (physics)Parallel manipulatorEngineeringRobotAdaptive controlTorqueStability (learning theory)Control (management)Computer scienceMobile robotArtificial intelligenceMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

Rotor flying manipulator (RFM), a new kind of mobile robot system being composed of a rotor flying robot (RFR) and a (several) manipulator(s), has absorbed great attentions in recent years because it enables a RFR to complete active tasks such as mastering and transporting objects. However, steady control of the RFM during both maneuvering and operating is of great difficulty due to the heavy force/moment coupling between the RFR and the manipulator, which introduce high nonlinearity and complexity to the system model. In this paper, a new control scheme of the RFM is proposed to ensure the steady flight. Firstly, the nonlinear mathematical model is derived, which is followed by the coupling analysis to show how the movement of the manipulator influences the whole system's behavior. Subsequently, based on these analyses, a new control scheme is designed with obtainable coupling force/moment. The basic idea of the controller is to take the coupling as a disturbance and separately control the RFR and the manipulator, and the stability is ensured to properly design the controller parameters. Finally, simulations are conducted and the results show the feasibility and validity of the proposed controller.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

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