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Record W2168903798 · doi:10.2514/6.2009-6185

Robust Adaptive Reconfigurable Control for a Hypersonic Cruise Vehicle

2009· article· en· W2168903798 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 Guidance, Navigation, and Control Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsCruise controlCruiseComputer scienceHypersonic flightAeronauticsAdaptive controlCruise missileAerospace engineeringHypersonic speedControl (management)Automotive engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The use of indirect adaptive methods to augment the nonlinear Dynamic Inversion (DI) algorithm for actuator failure reconfiguration is investigated in this paper. The ability of the adaptive reconfigurable DI algorithm to tolerate different types of actuator failure is illustrated by using a hypersonic cruise vehicle model. With an actuator failed, the on-board aerodynamic model of the DI algorithm is adapted to the vehicle performance using an online parameter identification algorithm based on a Kalman filter approach. A reconfigurable control allocation algorithm based on the weighted pseudo-inverse approach is used to redistribute the actuator commands to the remaining healthy control surfaces. To explicitly quantify the stability and performance robustness properties, the Structured Singular Value (SSV), or P-analysis, in combination with the DI controller is first formulated. Improved robustness properties of the adaptive reconfigurable controller are demonstrated through P-analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
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 head0.023
GPT teacher head0.214
Teacher spread0.191 · 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