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Record W2997809692 · doi:10.2514/6.2020-2074

Trajectory Tracking Control of Highly Maneuverable Fixed-Wing Unmanned Aerial Vehicles

2020· article· en· W2997809692 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)TrajectoryControl engineeringThrustAttitude controlComputer scienceEngineeringAerospace engineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Agile fixed-wing aircraft combine the fast and efficient characteristics of conventional fixedwing platforms with the high maneuverability and precision of rotorcraft. To expand their range of application, a key challenge is the development of control systems that harness this increased maneuverability, with the goal of enhancing or expanding the possible motions of conventional fixed-wing flight. Thiswork presents a control architecture that enables agile fixedwing platforms to track a time-parametrized, three-dimensional position trajectory which is unconstrained by common limitations arising from an aircraft kinematic model assumption, including minimum airspeed, heading rate, and climb rate. The proposed system is a cascaded controller designed in two parts. First, a singularity-free, quaternion-based inner attitude control loop is designed to track attitude references. Then, an outer position control loop is constructed to determine the required thrust force and reference attitude that will drive the inertial position errors to zero. This outer loop is a hybrid controller, consisting of a control strategy for the steady regime and another for agile maneuvers. A switching logic is derived to ensure that any change of position controller occurs when the two stability regions overlap, retaining stability for the overall system. The resulting controller does not require any attitude reference to perform aggressive maneuvers, greatly simplifying the trajectory generation problem. Controller performance is first verified through numerical simulation using a high-fidelity aircraft model. A Software in the Loop simulation, which accounts for sensor noise, state estimation, and discrete-time implementation of the control algorithm, is performed before finally implementing the control system on a physical platform. Preliminary results show the control system enables the aircraft to perform a series of maneuvers often deemed infeasible for fixed-wing aircraft, thus greatly enhancing the usefulness of the platform.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.204
Teacher spread0.192 · 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