Research on compliant control strategy of grinding robot based on model adaptive impedance control
Bibliographic record
Abstract
Impact occurs in the transition stage when a grinding robot enters a constrained-movement space from a free-movement space. Excessive impact forces result in an unstable robot. Hence, a dynamic model of the contact between the robot end-effector and workpiece is established, and the steady-state error of the system is analyzed. Subsequently, to improve the robot’s compliance in unknown environment, the adaptive control algorithm is integrated with the impedance control strategy to construct the model adaptive impedance control algorithm. The stability of the algorithm is analyzed using the Lyapunov stability theory, and the optimization adjustment rules for each parameter are derived. Finally, a simulation model is established using the Matlab software. Simulations are conducted in different environments to verify the effectiveness of the proposed algorithm. The practicality of the validation method is experimentally demonstrated. The results show that the proposed algorithm can better reduce the effect of load on the performance of the grinding robot as well as improve the robustness of the grinding robot in managing various types of disturbances and uncertainties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".