Output-Driven Optimal Control of a Class of Nonlinear Systems Using Koopman Operator and High-Gain Observers
Bibliographic record
Abstract
We design an output-feedback optimal tracking controller for a class of nonlinear systems that possess full relative degree. The design procedure follows the standard LQT method using an approximate linear model of the system obtained by following the Koopman operator theory. We further identify the observables used for the Koopman method relying on output measurements only, leading to minimal data collection costs. We achieved this latter objective by realizing that output derivatives can be a good choice as observables, and hence, using high-gain observer to provide estimates of these derivatives. Overall, the proposed approach allows for solving the problem of optimal control of the considered class of nonlinear systems without the need for a prior knowledge of the system model. That is, this problem is solved and the controller is driven solely based on output measurement. We demonstrate the efficacy of the closed-loop system in controlling a power system with an infinite bus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".