Utilizing linear quadratic regulator and model predictive control for optimizing the suspension of a quarter car vehicle in response to road excitation
Bibliographic record
Abstract
Vehicle suspension systems are fundamental components designed to mitigate the adverse effects of road surface irregularities.These systems are typically categorized as passive, semi-active, or active suspensions.This study focuses on a quarter car suspension model to explore the application of two control methods, the Linear Quadratic Regulator (LQR) and the Model Predictive Control (MPC).Experimental data are collected using the Quanser active suspension experiment setup.Initially, the LQR controller is employed to optimize performance criteria related to the system state and input signals.Subsequently, the widely recognized MPC approach is used as an alternative control method.A comprehensive comparative analysis is conducted, taking into account various load conditions and parameter variations.Additionally, the study investigates system responses under varying road conditions, changes in plant characteristics, and the introduction of disturbances, to provide an exhaustive comparison of the two control methods.The results obtained with the MPC and the comparison with the findings of various authors to date allow us to emphasize that the presented results in this study significantly outperform the previous work.These outcomes have undergone rigorous validation on the physical model available in our mechatronics laboratory.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".