Reduction of the effect of actuator saturation with periodic servo-constraints
Bibliographic record
Abstract
Saturation is an undesired event in trajectory tracking control of mechanical systems. When the actuators of a robotic device saturate, the solution of the inverse dynamics problem cannot fully be realized, which results in deviations from the desired trajectory and loss of performance. It is generally hard to consider the limited actuator torques and the corresponding nonlinear effects in the control design. The most common way to handle the problem is recalculating the control forces and trying to adjust the desired trajectory such that saturation will not happen. In contrast we propose a switched control approach, where, upon saturation, different sets of inputs are varied periodically to keep the reference point of the robot on the desired trajectory. For this, the desired motion is formulated by means of servo-constraints, and the periodic switching of these constraints is adjusted according to the variation of a new, manipulability type performance measure. It is demonstrated that the proposed controller can effectively reduce the trajectory following error due to actuator saturation. A typical robotic benchmark example is provided to show the application of the method, and to compare it with other approaches taken from the literature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".