Dynamic Modelling and Adaptive Control of Automobile Active Suspension System
Bibliographic record
Abstract
The active suspension system of automobiles has great advantages in riding comfort and handling stability.However, it is a challenging task to design an active control method for this system, owing to system features like multi-input and multi-output, time variation, and nonlinearity.To cope with the challenge, this paper mathematically models the active suspension system based on the full-car model, rather than the common quarter car model, and obtains a nonlinear dynamic model with variables like displacement, roll angle and pitch angle.Subsequently, an incremental proportional-integral-derivative (PID) controller was designed, and a deep reinforcement learning adaptive (DRLA) controller was proposed to realize online adjustment of control parameters.Finally, the active suspension system of the entire vehicle was simulated on MATLAB/Simulink.The simulation results prove that the DRLA controller can effectively reduce the displacement, the amplitude of roll and pitch angle of the car body, and greatly enhance the smoothness of the ride on the vehicle.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".