Adaptive Fuzzy Tracking Control of Flexible-Joint Robots Based on Command Filtering
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Bibliographic record
Abstract
The precise tracking control problem for n-link flexible-joint (FJ) robotic systemsis addressed in this paper. A new adaptive fuzzy command filtered control strategy is presented, where fuzzy logic systems are utilized to approximate the unknown nonlinearities of FJ robot systems. Compared with existing backstepping-based methods, the proposed scheme can not only overcome the so-called “explosion of complexity” problem, but also reduce filter errors because of the introducing of an error compensation mechanism. Moreover, regardless of the number of fuzzy rules, only one parameter is required to be adjusted online, which reduces significantly the computational cost. The proposed scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The simulation results of a two-link robot system confirm our theoretical analysis and a comparison study demonstrates the advantages of the design method in comparison with existing results, such as the backstepping method and the dynamic surface control method.
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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) | 0.001 | 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 | 0.001 |
| 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