Nonlinear multi-objective receding horizon design utilizing state-dependent formulation
Bibliographic record
Abstract
The art of multi-objective design is to extract the best compromise among conflicting requirements. The analytical multi-objective parameter synthesis adopts closed-form specifications that make the multi-objective design in linear systems computationally efficient. In this paper, the analytical multi-objective parameter synthesis method is extended to nonlinear systems via the state-dependent coefficients parametrization method in combination with the receding horizon control technique. The novel contributions presented in this paper are demonstrated on two fronts. First, the objective functions in infinite-time domain are replaced by equivalent finite-time functions with certain terminal terms, where the analytical formulations for the finite-time functions are made available and offer a computationally cost-effective approach. Second, the stability is addressed by adopting the receding horizon control technique. In addition, the proposed design approach shows benefits over the conventional nonlinear optimal control method. Two numerical examples further demonstrate the promising features of the proposed design framework.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".