Rejecting the effects of both input disturbance and measurement noise: A second‐order control system example
Bibliographic record
Abstract
Summary This article originates from the well‐accepted observations in practice: rejection of both input disturbance and measurement noise is practically important for high‐precision tracking control, and the classic estimators, such as the uncertainty and disturbance estimator (UDE) and disturbance observer, are proven to be inherently sensitive to measurement noises. Motivated by these observations, we develop a robust control solution and demonstrate the possibility of unifying the design of noise estimator (NE) and UDE for a class of second‐order systems. Interestingly, the NE and UDE have three important features in common: (i) the designs are based on system model and reliable state measurement; (ii) a first‐order filter is used to ensure that the design is physical realizable, rather than to filter out undesired signals; (iii) the filter parameters are readily determined by an introduced singular perturbation parameter. The performance of UDE is improved when augmented with NE to reject measurement noises. Then, a simple mapping for parameter tuning is presented, by which the estimation performance can be explicitly analyzed using the singular perturbation theory. Comparative simulation and experimental studies show that the proposed NE+UDE‐based solution is not only less sensitive to measurement noise than the classic UDE‐based control, but also able to deliver superior trajectory‐tracking performance over other robust output feedback control approaches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".