Bibliographic record
Abstract
This paper addresses analysis and design issues in adaptive PID control for linear second-order minimal phase processes using the backstepping algorithm. The first step consists in adding an integral action to the basic backstepping algorithm to obtain a zero static error. An integrator is therefore added to the plant model and is then slid back to the controller equation at the end of the design. The control law is made adaptive without using a certainty-equivalence design and is robustified even more with nonlinear damping. The resulting adaptive PID control is uce+udyn+unld , where uce is what would be the output of the adaptive PID if a certainty-equivalence-based design were used, udyn compensates for the adaptation dynamics and unld is a nonlinear damping term added to increase the robustness by bounding the errors, even when the adaptation is off. The resulting PID controller is hence more robust and presents better transients than the basic certainty-equivalence PID version. An example compares the proposed PID to a certainty-equivalence PID.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".