Adaptive control and reinforcement learning for vehicle suspension control: A review
Bibliographic record
Abstract
The growing adoption of electric vehicles has drawn a renewed interest in intelligent vehicle subsystems, including active suspension. Control methods for active suspension systems have been a research focus for many years, and with recent advances in machine learning, learning-based active suspension control strategies have emerged. Classically, suspension controllers have been model-based and thus limited by necessarily simplified models of complex suspension dynamics. Learning-based methods address these limitations by leveraging system response measurements to improve the system model or controller itself. Previous surveys have reviewed conventional and preview-based active suspension controllers, but a detailed examination of newer learning-based methods is lacking. This article addresses this gap by presenting the mathematical foundations of these controllers and categorizing existing implementations. The review classifies learning-based suspension control literature into two main categories: adaptive control, which emphasizes stability through online learning, and reinforcement learning, which aims for optimality through extensive system interactions. Within these broader domains, various sub-categories are identified, allowing practitioners and researchers to quickly find relevant work within a specific branch of learning-based suspension control. Furthermore, this article discusses current trends in the field and proposes directions for future investigations. These contributions can serve as a comprehensive guide for the future research and development of learning-based suspension controllers. • Reviews reinforcement learning-based active and semi-active suspension controllers. • Reviews adaptive control-based active and semi-active suspension controllers. • Identifies future research opportunities in the domain of learning-based suspension control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".