Modulation Control of Bi-State Adaptive Impedance Device for Active Vibration Suppression
Bibliographic record
Abstract
The piezoceramic actuator based Smart Spring is a bi-state or binary impedance device used for active vibration suppression by means of semi-active control. A modulation control scheme for achieving quasi-continuous variation of stiffness using such a bi-state stiffness device is proposed in this paper. A two degree-of-freedom system model that incorporated a variable stiffness member was used as the demonstration platform to evaluate control strategies. Dynamic simulations of the model using Matlab and Simulink were performed to demonstrate the effectiveness of candidate control laws. The bi-state system subjected to a disturbance force exploits the use of a continuously varying stiffness member by means of the feedback linearization technique applied to design a non-linear control law. The ability to control a discrete stiffness device in a quasi-continuous manner offers the opportunity for the application of a larger selection of control approaches than the state-switched type control laws currently employed with such devices. In addition, the performance of the resulting controller compares favorably to that obtained using a state-switched control law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".