Backlash-Compensated Active Disturbance Rejection Control of Nonlinear Multi-Input Series Elastic Actuators
Bibliographic record
Abstract
Series elastic actuators with passive compliance have been gaining increasing popularity in force-controlled robotic manipulators. One of the reasons is the actuator's ability to infer the applied torque by measuring the deflection of the elastic element as opposed to directly with dedicated torque sensors. Proper deflection control is pinnacle to achieve a desired output torque and, therefore, small deviances in positional measurements or a nonlinear deformation can have adverse effects on performance. In applications with larger torque requirements, the actuators typically use gear reductions which inherently result in mechanical backlash. This combined with the nonlinear behaviour of the elastic element and unmodelled dynamics, can severely compromise force fidelity.This paper proposes a backlash compensating active disturbance rejection controller (ADRC) for multi-input series elastic actuators. In addition to proper deflection control, a multiinput active disturbance rejection controller is derived and implemented experimentally to mitigate any unmodelled nonlinearities or perturbations to the plant model. The controller is experimentally validated on a hybrid motor-brake-clutch series elastic actuator and the controller performance is compared against traditional error-based controllers. It is shown that the backlash compensated ADRC outperforms classical PID and ADRC methods and is a viable solution to positional measurement error in elastic actuators.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".