The comparison respond of braking torque control between PID and SMC controller for electric powered wheelchair descending on slope condition
Bibliographic record
Abstract
During descending on a slope, the speed of Electric Powered Wheelchair (EPW) tends to changed rapidly. Normally, most EPW is provided with mechanical braking system which transfers human pulling force of the lever creating friction at the tire. However, the task is difficult for the users are elderly or paralyses. However, even for normal user with good strength, in fear condition they tend to give sudden braking which leads to tire locking up and skidding, eventually EPW unstable. These problems will cause accident and injuries to the users if speed does not properly control. In this paper, the automated braking torque control method was proposed in EPW as alternative to solve this problem and increase the mobility and stability especially during descending on slope in other to help the user of the EPW as their daily transportation. In this research, Proportional-Integral-Derivative and Sliding Mode Control controller are compared to determine the best response for torque braking control. The rapid change of speed can be controlled by the braking torque using proposed controllers based on the desired constant speed set by the control designer. Moreover, the sudden braking that caused tire to lock up and skid can be avoided. Furthermore, result from SMC shows this controller have good time respond to maintain the speed based on desired value when descending at slope condition by controlling the braking torque compared to the PID controller.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".