Uncertainty models and robust complex-rational controller design for flexible structures
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it