Robust Gear Shifting Force Control of a Solenoid Actuator in an Automated Manual Transmission of an Electric Vehicle via µ-Synthesis
Bibliographic record
Abstract
This paper proposes a multi-physics approach to the force control of an electromagnetic solenoid system utilized to perform the gear shifting in an electric vehicle equipped with an automated manual transmission. Considering different operating conditions of the electric vehicles, the operating temperature of the gear shifting system varies on the time scales of minutes, hours, days and seasons. Such a temperature variation affects the performance of the gear shifting process in electric vehicles which are equipped with automated manual transmissions and causes a considerable uncertainty in the dynamical behavior of the gear shifting system. The aim of the present study is to develop a control strategy to perform an efficient gear shifting in different operating temperatures. To this end, a coupled thermal-electromagnetic modeling approach is followed to identify the uncertainty model and to model the perturbed systems. The accuracy of the obtained uncertainty model is verified by a set of experiments. Knowing the perturbed systems, the μ-synthesis robust control technique is employed to design a robust closed-loop force control system which ensures a satisfactory gear shifting over the entire range of operating temperatures. The simulation results validate the robustness, performance, and stability criteria.
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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 | 0.000 |
| 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) | 0.001 | 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".