Unidirectional virtual inerter for high-bandwidth robot motion control
Bibliographic record
Abstract
This paper introduces a Unidirectional Virtual Inerter (UVI) as a novel feedback control element in conjunction with a traditional PD controller for high-bandwidth robot motion control. Designed to harness the beneficial properties of a physical inerter within a digital framework, a UVI overcomes the limitations inherent in the physical domain, such as mechanical design complexity, significant weight and size, and maintenance requirements. The research emphasizes the exploitation of the energy dissipation capability that emerges as the inerter transitions from a physical to a discrete setting. Although inerters are traditionally viewed as energy storage devices, their adaptation to the digital domain reveals a promising energy dissipation function. Moreover, by utilizing the features of the VI in a unidirectional manner, this study delves into the UVI’s unique advantages in the digital realm, especially its remarkable energy dissipation ability and the dynamic adjustment of gains based on the system’s kinetic energy. This innovative approach, designed to enhance the performance of existing derivative controllers, significantly improves system convergence speed and enhances stability by dissipating the system energy. The paper includes a stability proof using a common Lyapunov function and validates the effectiveness of the UVI in enhancing system stability and tracking accuracy through simulations and experiments with a multi-DoF robotic manipulator. The findings particularly underscore the controller’s efficacy in regulation and trajectory-tracking tasks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".