A differential-algebraic approach for robust control design and disturbance compensation of finite-dimensional models of heat transfer processes
Bibliographic record
Abstract
Control design for heat transfer processes usually has to deal with significant uncertainty in parameters of finite-dimensional system models. These finite-dimensional models are used as an approximation for the underlying infinite-dimensional representation of the system dynamics governed by partial differential equations. To obtain control laws that can be evaluated in real time, the infinite-dimensional representation usually has to be replaced by a finite-dimensional one. However, the resulting approximation errors as well as the parameters characterizing heat transfer and heat conduction properties are typically not directly measurable in experiments. Therefore, control strategies have to be derived that are able to cope with the before-mentioned sources of uncertainty. In this paper, a robust combination of feedforward and feedback control laws is derived that guarantees asymptotic stability and accurate trajectory tracking. The robustness of the control structure is obtained by an offline control synthesis by means of linear matrix inequalities for a linear system model with polytopic uncertainty. Moreover, an efficient approach for solving high-dimensional and high-index differential algebraic equations, implemented in DAETS, is employed to numerically compute dynamic feedforward control sequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".