Optimum Active Vehicle Suspensions With Actuator Time Delay
Bibliographic record
Abstract
A new approach to optimal control of vehicle suspension systems, incorporating actuator time delay, is presented. The inclusion of time delay provides a more realistic model for the actuators, and the problem is viewed from a different perspective rather than the conventional optimal control techniques. The objective here is to select a set of feedback gains such that the maximum vertical acceleration of the sprung mass is minimized, over a wide band frequency range and when subjected to certain constraints. The constraints are dictated by the vehicle stability characteristics and the physical bounds placed on the feedback gains. Utilizing a Simple Quarter Car model, the constrained optimization is then carried out in the frequency domain with the road irregularities described as random processes. Due to the presence of the actuator time delay, the characteristic equation is found to be transcendental rather than algebraic, which makes the stability analysis relatively complex. A new scheme for the stability chart strategy with fixed time delay is introduced in order to address the stability issue. The stability characteristics are also verified utilizing other conventional methods such as the Michailov technique. Results demonstrate that the suspension system, when considering the effect of the actuator time delay, exhibits a completely different behavior.
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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 | 0.001 |
| 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 | 0.000 | 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".