Design and Implementation of an Emotional Learning Controller for Force Control of a Robotic Laparoscopic Instrument
Bibliographic record
Abstract
Purpose: Force control of robotic instruments is a difficult task due to the uncertainties caused by changes in the instrument’s geometrical and mechanical characteristics during surgery as well as the nonlinear dynamics of the instrument. A new approach based on an intelligent controller is developed to control the force interactions of a robotic surgical instrument with delicate soft tissues. This feature assists the surgeon by providing a safe grasp of soft tissues during dissection or suturing. Besides, by controlling and optimizing the magnitude of the instrument/tissue contact forces, controlled grasp will significantly reduce the surgery trauma. Method: The controller is devised using a neuro-fuzzy regulator that receives the tracking error and its derivative as inputs, and a PD critic that evaluates the actual pinch force and produces an emotional signal. The controller tunes its parameters by means of minimizing the critic’s output signal, i.e., stress, so that the force tracking error is reduced. Numerical simulations and experimental tests were performed to evaluate the controller. Results: Simulation tests revealed that the controller can effectively adapt its rules when the instrument’s geometry and frictional behavior changes. The experiments revealed a settling time of 0.7 s with 3.1 % overshoot. In comparison with a PID, the proposed controller reduced
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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) | 0.000 | 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 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".