On robust controllers for active steering systems of articulated heavy vehicles
Bibliographic record
Abstract
This paper examines the robustness of different controllers for active steering systems (ASSs) of articulated heavy vehicles (AHVs) in terms of the directional performance. Controllers based on the linear quadratic regulator (LQR) technique were designed for ASSs. The success of the LQR-based controllers is dependent on the accuracy of linear models for AHVs. When designing ASS controllers, linearisation of the AHV models is usually necessary; this results in model inaccuracy and un-modelled dynamics, and the robustness of the LQR-based controllers may be degraded. ASSs for AHVs are assessed in the time-domain, which may lead to an incomplete performance evaluation. This paper assesses the robustness of the ASS controllers designed with the techniques of sliding mode control (SMC), nonlinear sliding mode control (NSMC), and mu-synthesis (MS). The ASS controllers are evaluated using numerical simulation in terms of the trade-off between the manoeuvrability and the lateral stability at high speeds.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".