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Point-to-Point Motion Trajectory Generation for Uncertain Systems: A Closed-Form Solution

2023· article· en· W4382936244 on OpenAlexaff
Yazan M. Al-Rawashdeh, Mohammad Al Janaideh, Marcel Heertjes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
Fundersnot available
KeywordsControl theory (sociology)BacksteppingObserver (physics)Controller (irrigation)Euler anglesTrajectoryComputer scienceUnderactuationAngular velocityNonlinear systemCascadeSeparation principleState observerMathematicsAdaptive controlControl (management)EngineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper investigates output feedback tracking control of Unmanned Aerial Vehicles using only the measured inertial coordinate. The output feedback control is based on two cascade high-gain observers combined with a full-state feedback control that is based on the backstepping approach. In this work, the backstepping controller is designed to solve the tracking control problem of the underactuated system. Then, an observer comprised of two cascaded high-gain observers with different speeds is considered; the faster observer provides estimates of the output position and velocity of the system in three dimensions and feeds a virtual nonlinear output to estimate the Euler angles (pitch, roll, and yaw) and angular velocity. We show that the equilibrium point of the full-state feedback control system under full knowledge of the system information is exponentially stable. The simulation results show that the output feedback control achieves the tracking control objective and recovers the performance of the state feedback control. Also, the simulation results show convergence and the boundedness of the estimation errors.

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 categoriesnone
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 score0.938
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.044
GPT teacher head0.258
Teacher spread0.214 · 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
Published2023
Admission routes1
Has abstractyes

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