A simple fuzzy control design for powertrain systems with three inertias
Bibliographic record
Abstract
This paper proposes a simple fuzzy control design for powertrain systems with three inertias. Considering four operating modes of the clutch, the piecewise affine state-space models offers an accurate characterization of the powertrain systems as controlled processes. A class of Takagi-Sugeno fuzzy controllers (T-S FCs) is offered with this regard. The inputs of the T-S FCs are the two variables that define the four operating regimes and they also define a partition of the fifth order state-space model, the control error and the increment of control error. The control error and the increment of control error are usually involved in structures of Mamdani and Takagi-Sugeno PI-fuzzy controllers. The control error is defined considering the wheel speed as the controlled output. Neglecting the affine terms in the piecewise affine state-space models of the process, the frequency domain design is applied to the process models in terms of neglecting the affine terms to obtain four continuous-time PI controllers. The continuous-time PI controllers are next discretized and included in the six rule consequents of T-S FCs. This modal equivalence principle-based design approach offers T-S FCs that exhibit the bumpless interpolation between separately designed linear PI controllers for each operating mode of the clutch. Digital simulation results are presented to illustrate the performance of the fuzzy control system and to compare it with the linear PI-based control system that are successfully used as wheel speed control systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".