Adaptive prescribed performance control with selected transient response for a class of nonlinear systems with uncertainties
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Bibliographic record
Abstract
Summary This article proposes a novel adaptive prescribed performance control method. Featured with a selected transient performance, the proposed control method can achieve prescribed performance control by a new error transformation method. With the proposed control strategy, the closed‐loop system can follow the prescribed performance with a predefined curve. An adaptive controller is constructed based on the adaptive backstepping technique. By utilizing the Lyapunov stability, asymptotic stability is achieved for the closed‐loop system. Two examples with simulation results are provided to illustrate the proficiency of the proposed control strategy. To make comparisons, the same second‐order transient response is adopted as the performance function for both examples. The selection of gains and parameters are investigated by tests. The expected prescribed performance and the asymptotic stability are achieved in both examples, which verifies the proposed control strategy. Some discussions and comparisons are made accordingly as well.
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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 it