Decentralised connectively finite-time control for a class of p-normal form nonlinear large-scale systems with expanding construction and its application
Bibliographic record
Abstract
In this paper, the decentralised connectively practical finite-time control problem is studied for a class of p-normal form large-scale systems with expanding construction. First, the decentralised connectively practical finite-time controllers are designed for the p-normal form large-scale systems without expanding construction by combining adding a power integrator technique, the backstepping method, the Lyapunov theory with the neural adaptive technology. The designed controllers can guarantee that all the signals of the closed-loop system are practically finite-time stable and the large-scale system is connectively stable. Then, the expansion of the system is considered. A new subsystem is added to the original system online. It is needed that the decentralised control laws and the adaptive laws of the original system are kept to be unchanged, and only the control laws and the adaptive laws for the newly added subsystem need to be designed. Under the premise, the control laws and the adaptive laws of the new subsystem are designed, which can guarantee that both newly added subsystem and resultant expanded closed-loop large-scale system are connectively practically finite-time stable. The singularity problem arising in the design process for practical finite-time control is solved. Here, the adding a power integrator technique is applied to handle the control design problem for p-normal form systems. And the control laws and the adaptive laws are simplified by neural networks. The two numerical examples including an actual double-inverted pendulum system connected by a spring are presented to show the effectiveness of the proposed control 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 | 0.000 | 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 | 0.001 | 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".