Fuzzy sliding mode control based on longitudinal force estimation for electro-mechanical braking systems using BLDC motor
Bibliographic record
Abstract
This paper focuses on the controller design using fuzzy sliding mode control (FSMC) with application to electro-mechanical brake (EMB) systems using BLDC Motor. The EMB controller transmits the control signal to the motor driver to rotate the motor. The torque distribution of motors is studied in this paper actually. Firstly, the model of the EMB system is established. Then the state observer is developed to estimate the vehicle states including the vehicle velocity and longitudinal force. Due to the fact that the EMB system is nonlinear and uncertain, a FSMC strategy based on wheel slip ratio is proposed, where both the normal and emergency braking conditions are taken into account. The equivalent control law of sliding mode controller is designed on the basis of the variation of the front axle and rear axle load during the brake process, while the switching control law is adjusted by the fuzzy corrector. The simulation results illustrate that the FSMC strategy has the superior performance, better adaptability to various types of roads, and shorter braking distance, as compared to PID control and traditional sliding mode control technologies. Finally, the hardware-in-loop (NIL) experimental results have exemplified the validation of the developed methodology.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".