Hybrid Active–Passive Robust Control Framework of a Flexure-Joint Dual-Drive Gantry Robot for High-Precision Contouring Tasks
Bibliographic record
Abstract
For high-precision contouring tasks in a typical Cartesian motion system, multiaxis cooperation is a long-standing challenging issue. Inevitably, various factors pose substantial difficulty in the multiaxis cooperation leading to degraded contouring performance, such as the strong coupling effect between different axes, nonlinearity, the unknown dynamics due to the friction, and the difficulties in accurate system identification. To enhance the contouring performance of a flexure-joint dual-drive gantry system against the aforementioned issues, this article presents a hybrid active–passive robust control framework leveraging a model-free architecture. In this control scheme, all the coupling effects, nonlinearity, disturbance, and unknown dynamics are considered as “lumped uncertainty”. Then, a super-twisting sliding mode control method with a signum-type iterative learning law is proposed to passively suppress the lumped uncertainty during iterations; and an extended state observer is deployed to actively compensate the lumped uncertainty and ensure the establishment of sliding motion in the time domain. As supported by theoretical analysis, the proposed controller is shown to exhibit several important properties. First, the establishment of the sliding motion is guaranteed globally, in both the time domain and the iteration domain. Second, the properties of short establishment time of the sliding motion, fast convergence during the iterations, and low chattering are achieved. Moreover, a series of comparative experiments are conducted, and the proposed method is shown to be rather effective in achieving excellent contouring performance in the high-speed and complex-curvature contouring tasks, without relying on the system model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".