Unbiased Identification of Finite Impulse Response Linear Systems Operating in Closed-Loop
Bibliographic record
Abstract
The force and position data issued to construct models of joint dynamics are often obtained from closed-loop experiments, where the joint position is perturbed using an actuator configured as a position servo. If the position servo is orders of magnitude staffer than the joint, as is often the case, it is possible to treat the data as if they were obtained in open loop. It may be more relevant to study joint dynamics in compliant environments. This can be accomplished by adding an admittance controller, programmed to simulate a compliant environment, into the servo. Under these conditions, the presence of feedback cannot be ignored. Unbiased estimates of a system can be directly obtained from closed-loop data using the prediction error method. However, this is not true, in general, when linear regression or correlation-based analysis is used to fit nonparametric time- or frequency domain models. We develop a prediction error minimization based identification method for a nonparametric time-domain model, augmented with a parametric noise model. Simulations suggest that the method produces unbiased estimates of the dynamics of a system operating inside a feedback loop, even though linear regression results in substantial biases.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".