Robust H<inf>&#x221E;</inf> force control of a solenoid actuator using experimental data and finite element method
Bibliographic record
Abstract
In this paper, the robust force control of a solenoid actuator is studied. Such control problem is of interest in the study of gear shifting control in electric vehicles (EVs) equipped with an automated manual transmission (AMT). Experimental system identification together with the finite element method (FEM) is the approach considered in this paper to model the dynamic behavior of the solenoid actuator as well as the system uncertainties. Using experimental system identification, a dynamic model of the actuator is obtained and a nonlinear algebraic model of the electromagnetic force versus current and air gap is proposed. Using the properties of the magnetic materials and the geometry of the actuator, an FEM analysis is performed using Magnet®- Infolytica software - to obtain the dynamics of the nominal system and verify the system identification result. Considering the inherent uncertainty of the physical parameter involved in the actuation system as well as the measurement errors, an uncertainty analysis is performed to obtain the dynamic uncertainty model of the solenoid system. Moreover, considering the application of such actuator in the gear shifting process, the closed-loop performance objectives are defined with respect to the desired gear shifting quality. Knowing both the nominal system model and the uncertainty model, an H∞robust controller is designed. The performance of the resulting robust closed-loop control system is examined for the nominal and perturbed systems and is shown to satisfy the objectives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".