Adaptive control and reinforcement learning for vehicle suspension control: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| 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 | 0.001 |
| 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 it