Analysis and design of control systems via parameter‐based approach
Bibliographic record
Abstract
Analysis and design of control systems via parameter-based approachControl laws can be constructed in systems design by introducing parameters to obtain good system performance or robustness.A typical example of such a class of design approaches is the high-gain design approach.In the design of high-gain controllers, an introduction of a parameter results in a family of feedback control laws.Such a feedback law is given by a parametrized gain matrix which approaches infinity as the introduced parameter approaches its extreme value.Such a class of design approach can be called a parameter-based approach.Another case of the parameter-based approach is to establish mathematical models by introducing some extra parameters.A typical example is to derive the mathematical model of a spacecraft by unit quaternions which are constructed by introducing three extra parameters i, j and k with the propertiesThis Special Issue aims to present the recent development of the parameter-based approach in analysing and designing control systems.The theme of this special issue can be divided into three parts: Model construction by introducing parameters, control law design by introducing parameters and parametric solutions to control-related matrix equations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".