Backstepping adaptive position control for robotic manipulators
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents a general approach for the design of an adaptive position control for a robotic system by the backstepping strict-feedback technique. The advantage of this control technique is that it imposes desired properties of stability by fixing the candidate Lyapunov functions initially, then by calculating the other functions in a recursive way. The adaptation of the parameters is made by the direct adaptive method. The results obtained by using this control method, which is valid for various types of robotic architectures, are very satisfactory when applied to a three d.o.f robot consisting of one prismatic axis (axis 1), and two others rotary axes. The results show that the robot follows exactly the triangular trajectory assigned to him. The trajectory tracking errors are negligible. Simulation results are included in order to evaluate the tracking performances and the global stability of the controlled system, and to justify the reliability of the proposed control technique.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it