CMAC-Adaptive Force-Position Control of a Flexible-Joint Robot
Bibliographic record
Abstract
Although many hybrid force-position controllers appear in the literature, the problem of touching and leaving a surface rarely gets addressed - many leave this as a practical matter for the engineers. If the force control results in inappropriate signals in free space then the designer must try to switch controllers at the surface, a solution that can introduce unwanted vibrations; note that stability problems can easily result with such a design in light of imperfect knowledge/measurement of where the surface actually lies and the reality of (possibly unmodelled) joint elasticity. In this work we propose an adaptive backstepping approach that guarantees Lyapunov stability when in contact with the surface and in free-space i.e. without switching, for both non-redundant and redundant manipulators. We develop the controls for a flexible-joint robot in order to demonstrate the guarantee of stability and the ability to avoid excessive vibrations even in the case of elasticity. The proposed controls use neural networks to estimate nonlinear terms and unmodelled dynamics. Simulations show the proposed method significantly outperforms a proportional-derivative hybrid force-position control.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".