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Record W3177253845 · doi:10.2514/6.2001-4099

Uncertainty models and robust complex-rational controller design for flexible structures

2001· article· en· W3177253845 on OpenAlexaff
Nabil Aouf, Benoît Boulet

Bibliographic record

VenueAIAA Guidance, Navigation, and Control Conference and Exhibit · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsRobust controlComputer scienceRobustness (evolution)Control theory (sociology)Controller (irrigation)Control engineeringControl systemEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Realparametricuncertaintiesin themodal dampingratiosandfrequenciesofe exiblestructuresarerepresented by complex uncertainties that can lead to robust controller designs satisfying robust performance specie cations. Thesecomplex uncertaintyblocksareusefulina π-synthesiscontrollerdesignprocedure.Twomodelsareproposed for modal parameter uncertainties. The e rst model uses a coprime factorization representation of the perturbed plant, whereas in the second model, a diagonal representation with complex eigenvalues is used. The innovation in the second method proposed is the use of a complex-rational controller design strategy, which offers tight uncertainty bounds and leads to a robust performance controller. The frequency response of the complex-rational controller is then approximated by a real-rational controller achieving the robust performance specie cations. LEXIBLE structures are generally characterized by the un- dampednaturalfrequenciesand dampingratiosoftheire exible modes. These parameters are subject to errors when they are esti- mated. Such uncertainties are important and should be taken into account in a robust controller design. The proper capture of modal parameter uncertainties in dynamic models of e exible structures for robust control has been the subject of ongoing efforts. Previ- ous research 1;2 used additive or multiplicative uncertainty models to take into account the variation in the dynamics of the plant. An- other way is to use certain heuristics to facilitate the representation of the parametric uncertainties in the e exible modes by a para- metric model. 3 These heuristics represent approximations in the parameter variation that are not generally realistic and lead to con- servative controller designs, that is, designs that cannot provide the desired performance in the face of realistic levels of parametric uncertainty. Recently, a model representing parametric uncertainties in the modes of a e exible structure was discussed in Ref. 4. Note that such models were developed 5 a few years ago. In Ref. 5, a model of dynamic uncertainty covering parametric variations in the e exible modes of a e exible structure was developed. This dynamic uncer- tainty has the virtue of being nonconservative, but only when the frequencies of the e exible modes are close to each other. In this paper, we propose to represent perturbations in the frequency and damping ratio of each e exible mode by a tight low-order dynamic uncertainty. Thus, we reduce the order of the augmented plant by half and transform the mixed real/complex uncertainty robust per- formanceπ-synthesis problem into an easier complex π synthesis. We use two techniques: The e rst is based on the coprime factor- ization framework, 6 and the second uses a complex diagonal modal representation to model the dynamics of the e exible structure and

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)0.001
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.156
GPT teacher head0.325
Teacher spread0.170 · 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
GenreMethods

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

Citations1
Published2001
Admission routes1
Has abstractyes

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