GA-LQR for vehicle semi-active suspension with BiLSTM inverse model of magnetic rheological damper
Bibliographic record
Abstract
This paper proposes a magnetic rheological (MR) semi-active control method based on bidirectional long short-term memory (BiLSTM) neural network, linear quadratic regulator (LQR) control algorithm, and genetic algorithm (GA). The LQR algorithm with GA optimizing the weight coefficients generates the expected damping force. Due to the nonlinear hysteresis characteristics of the magnetic rheological damper (MRD) and the fact that its input and output have certain time dependence, an inverse model of MRD is established by BiLSTM. The control current is predicted by BiLSTM and then the current is input to the MRD to obtain the damping force that is infinitely close to the expected damping force. The damping force is then applied to the suspension system to form a complete closed-loop feedback control, which realizes the damping effect and generates a real-time control. The simulation results show that the MRD inverse model can accurately predict the required control current, and the GA-optimized LQR control algorithm has a good suppression effect on the vertical vehicle acceleration, dynamic tire load, and suspension dynamic stroke.
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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".