Semi-Active Control of a Two-Phase Fluid Strut Suspension via Deep Reinforcement Learning
Bibliographic record
Abstract
Gas–oil emulsion struts (GOESs), with their simplified and low-cost design and minimal friction, offer attractive potential for industrial applications. However, they exhibit highly nonlinear damping behavior due to the compressibility of the gas–oil emulsion. This study proposes a semi-active control strategy for modulating the emulsion flow via a dynamically controlled solenoid valve. The GOES is modeled considering pressure-dependent friction and flow characteristics. A reinforcement learning model is further developed to modulate the opening area of the control valve under random road excitations to enhance vibration ride comfort, using a quarter-vehicle model framework. The validated model is used to analyze the strut’s performance under three different scenarios, namely, the original passive, optimal passive, and semi-active. The results suggest that the proposed semi-active strategy could yield a considerably lower root mean square of the sprung mass acceleration for both the passive and optimal systems. It is further shown that real-time adjustment of the control valve could yield nearly 27.2% enhancement in ride comfort performance in comparison to optimal passive GOES.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".