A New Control Strategy for Coordinated Control of Ground Vehicle Vertical Dynamics via Control Allocation
Bibliographic record
Abstract
The scope of this thesis concerns the basic research and development of a coordinated \ncontrol system for the control of vehicle roll and pitch dynamics using suspension forces as actuators. In this thesis, the following question is explored: How can suspension control (particularly semi-active control) be generalized to control all vertical vehicle dynamics in a coordinated way, and which would ideally be integrable with modern ESC systems? The chosen approach to this problem will be the application of the control allocation methodology for overactuated systems, which makes use of online mathematical optimization in order to realize the desired control law. Background information on vehicle dynamics and modeling, suspension control, control allocation and optimization is presented along with a brief literature review. The coordinated suspension control system (CSC) is designed and simulated. High-level controllers and control allocators are designed and their stability properties are explored. Then, the focus is shifted towards implementation and experimentation of the coordinated control system on a real vehicle. The semi-active actuators are statistically modeled along with the deployed sensors. Both the hardware and software designs are explored. Finally, experiments are designed and results are discussed. Recommendations for future inquiry are given in the conclusion of this work.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".