Finite‐Time Adaptive Resilient Control With Prescribed Performance for Uncertain Nonlinear Systems Under False Data Injection and Actuator Faults
Bibliographic record
Abstract
ABSTRACT In this article, we present a novel adaptive resilient control strategy for a class of uncertain strict‐feedback nonlinear cyber‐physical systems (CPSs) suffering false data injection (FDI) and actuator faults. In contrast to most studies on nonlinear systems, the unknown state‐dependent gains are considered during system modeling. By formulating convergence‐region‐related piecewise functions, we construct a novel Lyapunov candidate. Then, an adaptive resilient controller, which is proved to guarantee both prescribed performance and finite‐time stability of this system, is subsequently developed using the dynamic surface control (DSC) technique. Specifically, Nussbaum functions and fuzzy logic systems (FLSs) are introduced to address the adverse effects induced by FDI and actuator faults. Through rigorous analysis, it is demonstrated that all signals within the closed‐loop system are semi‐globally bounded, and the state errors can be constrained within any predefined negative exponential function under appropriate parameters. In the end, the feasibility of this control strategy is verified through a simulation based on wing rock dynamics.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".