A New Integrated Skyhook-Linear Quadratic Regulator Coordinated Control Approach for Semi-Active Vehicle Suspension Systems
Bibliographic record
Abstract
Abstract Vehicle semi-active suspension systems can provide a satisfactory compromise of ride comfort and handling stability. With the rapid developments of damping control methodologies, ride comfort is expected to be improved in all three directions, i.e., vertical, roll, and pitch. Considering this kind of multi-objective coordinated control problem, Model Predictive Control (MPC) gains lots of attention in the recent decade. However, the MPC algorithm is expensive for real-time implementations due to its computational complexity. This paper introduces an integrated Skyhook and linear quadratic regulator (LQR) control approach, which can be easily processed on an automotive-grade microcontroller because of its concise algorithm. Meanwhile, it has great potential in attenuating vehicle body vibrations in vertical and rotational directions at the same time. Its control performances are evaluated through the co-simulation between carsim and matlab/simulink based on three different road conditions. The Buick Enclave, which is a full-size sport utility vehicle (SUV), is modeled in carsim for evaluating the control performance. The results demonstrated the control efficiency of the integrated Skyhook-LQR approach, which is even better than MPC, especially in vertical and pitch directions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".