Model predictive-based tractor-trailer stabilisation using differential braking with experimental verification
Bibliographic record
Abstract
Different types of instability modes in tractor-trailer vehicles, including jackknifing and snaking, necessitate designing a fast and effective control strategy. In this paper, a model predictive controller (MPC) is developed to prevent these instability modes in a car-trailer vehicle as a specific form of tractor-trailer vehicles equipped with differential braking. The effectiveness of the control action when the differential braking is applied only to the tractor and only to the trailer is also studied comparatively. The developed MPC controller utilises an affine tyre force model, and the control actions are limited based on the capacity of the tyres. The aim of the controller is to ensure that the tractor and the trailer follow the desired yaw rate and the desired hitch angle, respectively. The controller performance is evaluated through experimental tests and simulations. Experimental tests are conducted on an all-wheel-drive electric Chevrolet Equinox and a student-built research trailer, both equipped with an independent braking module on each wheel. In the simulations, the controller is implemented in MATLAB/Simulink, and an experimentally validated CarSim model of the tested tractor-trailer vehicle is employed. The results show that the designed MPC controller effectively prevents both instability modes; however, differential braking has much more capacity when it is applied to the tractor.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".