Autonomous Vehicle Steering-based Feedback Linearization and Sliding Mode Control
Bibliographic record
Abstract
This study proposes a robust control approach for vehicular dynamic system speed control. The proposed method combines a sliding mode controller with a feedback linearization technique. Considering the high nonlinearity of the vehicle dynamic system model, feedback linearization is used to transform the vehicle dynamic system into a linear system. A Lyapunov theorem is used to approve the stability of the proposed controller. Moreover, a proportional integral derivative (PID) controller with genetic algorithms is used for comparison. The integral absolute error (IAE) is used as the performance comparison index between controllers. Simulation results show that the proposed method can achieve excellent performance with high robustness against external disturbance and system uncertainty. In the tracking case, the IAE value of the proposed controller is 2.3, whilst that of the PID is 15.2. Under external disturbance, the IAE values are 3.1 and 19.1 for the proposed controller and PID, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".