SEMI-ACTIVE CONTROL OF VEHICLE SUSPENSION WITH MAGNETO-RHEOLOGICAL DAMPERS:PART III—EXPERIMENTAL VALIDATION
Bibliographic record
Abstract
A hardware-in-the-loop(HIL) test and simulation platform is developed in the laboratory, so as to validate the performance characteristics of the proposed skyhook-based asymmetric semi-active controller in Part I, and examine the validity of the proposed MR-damper model in a system surrounding. A real-time monitor is designed to assess and monitor the responses of the quarter-vehicle model in the HIL platform, and to select the excitation, controller synthesis, and the output displays. A drive current circuit hardware employing PID feedback technique is developed to compensate for the time delays from the servo-controller and drive current circuit, in which a small resistance is integrated in the current amplifier circuit to provide the feedback signal. The experiments were performed to measure the responses of the quarter-vehicle MR-suspension models with fixed current and the proposed semi-active MR-damping variations, under harmonic, rounded pulse and random road excitations. The measured data were compared with the corresponding model results to examine the model and controller validity, and revealed generally good agreements in the model and tested results and very little sensitivity of the tested responses to variations in the sprung mass. The HIL test results validate the effectiveness of the proposed skyhook-based semi-active asymmetric controller and its high robustness against the vehicle load variations in view of the intelligent vehicle suspension design.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".