Sliding Sector Mode Control with Linear Parameter Varying Diesel Engine Model
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Automotive industry has been developing technologies to reduce exhaust emission. Moreover, developing an environmentally friendly diesel engine will become the most important issue in the future engine technology. A modern well-tuned diesel engine no longer smokes and the noise has been attenuated to more acceptable levels. In spite of these efforts, emission of particulates and nitrogen oxides (NOx) have not been equally successful. Therefore, the accurate control method of diesel engines is coming up in solving the problems related with NOx and PM. The diesel engine is a highly nonlinear system and the modeling parameters are changed significantly for different operating points. Therefore, it is very difficult to control the diesel engine system accurately. Every engine system model is actually composed of a variety of maps. And then, actuators like dc motors are controlled for transferring the appropriate flow rate to the manifolds. This method must be simple and robust for real world operating conditions. However, simple controllers don't work well in every working condition. Moreover, engine systems like the common rail system have some manufacturing error. Those problems make the engine control a very difficult task. This study focuses on the application of a sliding control method for the time varying diesel engine model. The time varying diesel engine model can reflect every engine operating point and schedule the controller for abnormal environments. In this study, a sliding sector mode control method is suggested for reducing the well known chattering problem of sliding mode control. Sliding sector mode is defined for several sliding hyperplanes which are verified by Liapunov inequality. A general sliding mode controller is designed for several but fixed engine operating points. Finally, a sliding sector mode controller is designed which is intended to work for general and wide engine operating range.</div></div>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".