Uncertainty models and robust complex-rational controller design for flexible structures
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".