Adaptive Secure Finite-Time Optimal Control of Unknown Nonlinear Systems With State Constraints via Generalized Fuzzy Hyperbolic Models
Bibliographic record
Abstract
In this article, a novel adaptive critic learning (ACL) framework is constructed for a class of nonzero-sum (NZS) differential games problem of unknown continuous-time (CT) nonlinear systems with state constraints. First, generalized fuzzy hyperbolic model (GFHM)-based identifiers are established to reconstruct the unknown system dynamics. Then, under the ACL framework, a critic network with secure finite-time experience replay turning law is developed for each player to acquire the Nash equilibrium point solution in finite time while the finite-time stability is guaranteed via Lyapunov analysis. Meanwhile, the persistence of excitation (PE) condition is no longer needed in this work, by introducing an easy-to-check rank condition. Furthermore, by incorporating the immediate cost function associated with each player and the control barrier function (CBF), the algorithm ensures that the system states evolve in a secure environment. Finally, two numerical examples are presented to demonstrate the validity of the developed scheme.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".