Output-Driven Optimal Control of a Class of Nonlinear Systems Using Koopman Operator and High-Gain Observers
Bibliographic record
Abstract
This paper presents an output-feedback optimal tracking controller for a class of unknown nonlinear systems possessing full relative degree. The design procedure follows the standard Linear Quadratic Tracking (LQT) method using an approximate linear model of the system obtained via Koopman operator theory. A key contribution lies in identifying suitable observables for the Koopman method using only output measurements, thereby minimizing data collection costs and eliminating the need for full state information. We achieve this by recognizing that output derivatives serve as effective observables for this system class and employ a high-gain observer (HGO) to estimate these derivatives from output data. Overall, the proposed approach enables optimal control of the considered nonlinear systems without requiring prior knowledge of the system model. The controller synthesis and implementation rely solely on output measurements. This makes the proposed control strategy completely output-driven. We demonstrate the efficacy of the closed-loop system in controlling both an academic example and a power system with an infinite bus. Numerical comparisons with traditional linearization-based control highlight the performance benefits of the proposed Koopman-HGO approach.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".