Adaptive Tracking Control of Hybrid Machines: A Closed-Chain Five-Bar Mechanism Case
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Bibliographic record
Abstract
This paper considers the trajectory tracking problem of hybrid machines. A hybrid machine here refers to a machine that is driven by the constant velocity (CV) motors and servomotors in a proper configuration. The hybrid machine is a meaningful tradeoff between task flexibility and power capacity. However, this system has brought a new challenge to control due to the velocity fluctuation in the CV motor. The velocity fluctuation problem is caused mainly by the uncontrollable input current and the time-varying workload. In addition, the dynamic parameters are uncertain, which further increases the control difficulty. In this paper, we propose an adaptive control law for the trajectory tracking and demonstrate the effectiveness of this control law on a 2-DOF closed-chain five-bar hybrid mechanism driven by one servomotor and one CV motor. The principle of the proposed controller is to properly design the servomotor control input that can compensate not only the uncertainty in the servomotor but also the uncertainty in the CV motor. By the proposed adaptive control law, it can be theoretically proved that the position/velocity tracking errors of the joint associated with the servomotor and the velocity tracking error of the joint associated with the CV motor are convergent to zero as time goes to infinity. Finally, the simulation examples are given to illustrate the effectiveness of the proposed 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 | 0.001 | 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 | 0.002 |
| 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