Design and Optimization of a Robust Active Trailer Steering System for Car-Trailer Combinations
Bibliographic record
Abstract
This paper presents a robust active trailer steering (ATS) controller for car-trailer combinations. ATS systems have been proposed and explored for improving the lateral stability and enhancing the path-following performance of car-trailer combinations. Most of the ATS controllers were designed using the linear quadratic regulator (LQR) technique. In the design of the LQR-based ATS controllers, it was assumed that all vehicle and operating parameters were constant. In reality, vehicle and operating parameters may vary, which may have an impact on the stability of the combination. For example, varied vehicle forward speed and trailer payload may impose negative impacts on the directional performance of the car-trailer combination. Thus, the robustness of the conventional LQR-based ATS controllers is questionable. To address this problem, we propose a gain-scheduling LQR-based ATS controller. In the design of the proposed ATS controller, at each operating point, the ATS controller is designed using the LQR technique. At an operating point between two established adjacent operating points, the control gain matrix of the controller is determined using an interpolation method. To further improve the directional performance of the car-trailer combination, the weighting matrices of the LQR controller are determined using an optimization algorithm, namely, Generalized Differential Evolution (GDE). The effectiveness of the proposed ATS controller is demonstrated using numerical simulation based on a car-trailer model.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".