The Effect of Time Delay of the Semi-Active Dampers on the Performance of On-Off Control Schemes
Bibliographic record
Abstract
With the new advancement in the vibration control strategies and controllable actuator manufacturing, the semi-active actuators (dampers) are finding their way as an essential part of vibration isolators, particularly in vehicle suspension systems. The currently available semi-active damper technologies can be divided into two main groups. The first uses controllable electromagnetic valves. The second uses magnetorheological (MR) fluid to control the damping characteristics of the system. Despite different semi-active control methods and the type of actuators used, one important practical aspect of all hydro-mechanical computer controlled systems is the time response (delay). The longest time response (delay) is usually introduced by the actuator (in this case, controllable actuator) in the system. This paper investigates the effect of time response in an on-off controlled suspension system using semi-active dampers. Numerical simulations and analytical techniques are deployed to address the issue. The performance of the system due to the time response is then analyzed and discussed. Specifically, the effect of the time response on the performance of a quarter car suspension system controlled by conventional on-off control strategies such as Skyhook and Rakheja-Sankar (R-S) is studied. Finally, the test results measuring time response in a newly developed semi-active actuator (namely, an external solenoid valve damper) is presented. Based on the real data and numerical simulations, the performance of a one degree of freedom quarter car system using this actuator is presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".