Weak disturbance decoupling of high‐order fully actuated nonlinear systems
Bibliographic record
Abstract
Summary It is well known that all practical control systems are subject to external disturbances. Therefore, a feedback controller should be designed so that the closed‐loop system has some desired properties such as stability, good tracking performance, at the same time, the impact of external disturbances on the performance of the closed‐loop system is attenuated to some degree. On the other hand, almost all models for real fully actuated systems, which are derived based on the physical laws, are described by a set of high order differential equations. The traditional method for controlling these fully actuated systems is to convert the high order differential equations to first order differential equations, which are called the state equations, and then to design feedback controllers by using the state‐space design methods. Such conversion causes some inconveniences. To overcome such inconveniences, a high‐order fully actuated (HOFA) design method has been proposed to control HOFA systems. The main goal of this paper is to design a weak disturbance decoupling controller for HOFA nonlinear systems to guarantee the stability and desired output tracking performance of the closed‐loop system and to attenuate the impact of disturbances.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".