Control of an electromechanical clutch actuator by a parallel Adaptive Feedforward and Bang-Bang controller: Simulation and Experimental results
Bibliographic record
Abstract
Vehicle’s powertrain performance and comfort are largely depending on the clutch control strategy for an Automated Manual Transmission (AMT). The aim of the clutch control strategy is to ensure the smooth running of clutch operational cases: a comfortable clutch launch (vehicle takes off smoothly without jerk), a fast upshift/downshift (gear ratio changes) and a fast clutch opening. In most industrial cases, regardless of clutch actuation technology, clutch control is managed by clutch pressure control. However, the clutch pressure control is a challenge regarding clutch non-linearities and time-varying parameters. In this paper, a parallel adaptive feedforward and bang-bang controller is proposed in order to control the clutch pressure with an electromechanical clutch actuator. In this system, a control issue comes from potential time-varying parameters but the main challenge comes from the hysteretic behavior of the system due to dry friction in the actuator assembly. An analytic model of the clutch and its electromechanical actuator including dry friction has been constructed and a prototype has been designed and integrated on a test bench. The parallel adaptive feedforward and bang-bang controller architecture and algorithms are developed. For three critical clutch operational cases, simulations and experiments have been run. Finally, in spite of time-varying parameters and a high hysteretic system behavior, simulation and experimental control results highlight that the controller allows a precise tracking of the pressure reference and a fast time response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".