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Record W2540770177

Fault tolerant control of large flexible space structures under sensor and actuator failures

2013· dissertation· en· W2540770177 on OpenAlexaff
E.J. Davidson, R.H. Kwong, Samuel T. C. Huang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)PID controllerActuatorEngineeringParametric statisticsFault toleranceControl engineeringControl (management)Computer scienceMathematicsArtificial intelligenceReliability engineering
DOInot available

Abstract

fetched live from OpenAlex

In this thesis, we study fault tolerant control (FTC) for the decentralized robust servomechanism problem (DRSP) of a colocated large flexible space structure (LFSS) under sensor and actuator failures (SAF). The control objective is to devise a decentralized con- troller that maintains the stability of the LFSS, tracks a constant reference for healthy outputs, regulates against an unknown constant disturbance for healthy outputs, and is robust against parametric uncertainties, so that “spillover effects” do not occur. Two FTC frameworks are considered: An active FTC framework that assumes SAF are known, and a passive FTC framework for which SAF are unknown. The active FTC framework ex- tends existing work on DRSP of a nominal LFSS, and applies a PID controller that has fault-dependent adjustments. Necessary and sufficient conditions for a solution to exist are determined, notably an easy-to-test rank condition. For the passive FTC framework, a PD controller that stabilizes an LFSS under unknown SAF is found. Although perfect tracking and regulation are not attained under the PD controller, by applying high gains, the errors for healthy outputs can be reduced to any desired level. However, outputs with failed sensors and healthy actuators can reach undesirably high magnitude under high gains. To improve performance under low gains, insights on steady-state outputs are applied to develop a feed-forward control that has good performance in tracking, but not regulation. Further analysis on the PD controller reveals a method to diagnose SAF using steady-state outputs. As a result, the PD controller and PID controller are found to have complementary advantages, leading to an 3-stage integrated FTC procedure. First, the PD controller can stabilize the LFSS under unknown SAF (passive FTC). Next, fault diagnosis is performed while the LFSS is stabilized. Finally, a reconfigured PID con- troller applying diagnosed SAF enables healthy outputs to meet control objectives (active FTC). Three examples, including a benchmark space platform with 200 states obtained by finite-element analysis, are used to illustrate the results throughout this thesis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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