Model predictive-based tractor-trailer stabilisation using differential braking with experimental verification
Why this work is in the frame
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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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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