Optimal tuning of H<sub>∞</sub> fixed‐structure robust controller against multiple high‐level requirements using evolutionary computation
Bibliographic record
Abstract
Summary This work deals with the optimal controller synthesis against high‐level multiple requirements using evolutionary computation. Indeed, such stochastic algorithms are interesting to solve problems based on complex industrial specifications and so seem to be particularly well suited to optimal robust controller synthesis. Using the H ∞ loop‐shaping framework, the optimal weights/controller tuning without any structural assumption (in terms of poles/zeros/damping) on the searched filters (except of course their order) is investigated. The absence of such any structural assumption is important to avoid affecting the quality of the solution toward a complex specification and allows reducing the synthesis problem to a simple one with static scalings in place of frequency weights. Using a version of differential evolution algorithm well adapted for high dimensional control problems, computing directly a fixed‐structure controller for complex industrial specifications toward a generic nonconstraint fitness with quite reasonable computing time is achieved. The illustrating example deals with the line‐of‐sight stabilization problem.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".