Decentralized Fuzzy Control of Multiple Cooperating Robotic Manipulators With Impedance Interaction
Bibliographic record
Abstract
In this paper, a decentralized adaptive fuzzy control has been developed for two cooperating robotic manipulators moving an object with impedance interaction. The contact forces are described using gradients of nonlinear potentials; then, the deformations of the contact surface can be obtained by an impedance approach. The cooperating manipulators are considered as a combination of subsystems, and the decentralized local dynamics coupled with physical interactions among the subsystems are developed. To compensate for the effect of dynamics uncertainties and external disturbances, decentralized fuzzy control combining parameter adaptations and disturbance observers is constructed. It guarantees the motion trajectories and impedance forces of the constrained object converging to the desired manifolds. It is theoretically established that the disturbance observers compensate for unparameterizable uncertainties, while the adaptive fuzzy mechanism compensates for the fast-changing components of the uncertainties that go beyond the disturbance observers. Moreover, unknown nonlinear dynamics such as the inertia matrix, Coriolis/centripetal matrix, and frictions, as well as interconnections with nonlinear bounds, can be accommodated through online learning. The experiments on two real robots have been carried out to verify the effectiveness of the proposed theoretical results.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".