Adaptive tracking control for nonlinear input-delay systems with full state constraints and unmodeled dynamics
Bibliographic record
Abstract
An innovative adaptive tracking control strategy is proposed in this paper for a class of nonlinear systems, which considers input-delay, full state constraints, and unmodeled dynamics simultaneously. To address the system’s unknown nonlinear dynamics, the approximation ability of multi-dimensional Taylor network (MTN) is employed in the controller design process. The effect of input-delay is reduced through the application of Pade approximation. Additionally, the impact of state constraints is mitigated through the introduction of barrier Lyapunov functions (BLFs). To deal with unmodeled dynamics, a dynamic signal is formulated. By integrating the backstepping control strategy with Lyapunov stability theory, it is ensured that all signals in the closed-loop system remain bounded, the tracking error approaches a small region close to the origin, and the full state constraints are not violated. Finally, simulation results are provided to validate the proposed strategy’s effectiveness.
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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 | 0.000 | 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 | 0.000 |
| 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".