An Iterative Learning Control Algorithm for Simulator Motion System Control
Bibliographic record
Abstract
In flight simulation, closed-loop motion cueing provides feedback to the pilot based on continuous control inputs. In some cases however, disturbances based on external triggers are also necessary. For example, taxiing over runway bumps, engine-related vibrations, and specific system failures cause awareness cues that are less dependent on the instantaneous control inputs by the pilot. These are motions that are triggered by specific events. Realizing the frequency content of these events in a temporally accurate way can be difficult, especially with limited motion platform dynamics. In addition, research simulators are often used for human perception experiments, where humans are subject to predefined waveforms. These waveforms are often significantly distorted by the dynamic response of the simulator’s motion system. An iterative learning controller was developed to improve the motion of a flight simulator in these situations. The controller was shown to significantly improve the response of the simulator to a jerk-limited acceleration square wave. Seven to ten iterations were required to converge to an acceptable response depending on the exact configuration of the controller. Rather than using a potentially destabilizing increase in feedback gain, the controller distorts the commands to achieve the desired response. The distorted commands can then be stored and later called upon to generate the desired motions as a function of a triggered input signal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".