Vehicle Longitudinal Control with the Sliding Mode Method Considering Uncertainty of the Model
Bibliographic record
Abstract
Abstract: In this paper, the control algorithm for longitudinal control of a passenger car that is moving on the highway and urban traffic is provided. Longitudinal dynamic equation including resisting forces acting on the vehicle presented and structural and non-structural uncertainties in the model are investigated. To control the speed of vehicles on the highway and vehicle distance control in city traffic, vehicle longitudinal control laws have been proposed. To control the speed of vehicles on the highway and in city traffic, vehicle longitudinal control laws have been proposed. In order to control the nonlinear systems, the sliding mode method which is a robust control method has been used for the design controller. The controller consists of two algorithms as speed control and distance control that their duty is done with the force generation to move the vehicle and overcome the disturbances and uncertainties using tracing of it by car. Finally, to ensure the ability of control and efficiency under different conditions, the control rules are applied to the vehicle dynamics equation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".