Optimal Backstepping Controller Design for Prosthetic Knee Joint
Bibliographic record
Abstract
The mobility of people who have had a lower limb amputated is slower, less stable, and needs more metabolic energy than the movement of physically fit, also, often have difficulty moving on uneven terrain and stairs. In most cases, these problems may be traced back to the usage of controllers for an above-knee prosthesis, which enhances movement and more quality of life for millions of individuals who have lost lower limbs. In this work addresses the dynamic modeling and parameter identification of the lower limb, and the control of a 2-DOF joint prosthetic, because the uncertainty, high nonlinearity, problems with imbalance, and external perturbations, which can occur during movement. Backstepping control algorithm based on the Lyapunov theory was used, this is to ensure system stability with enhanced dynamic performance. The Bat algorithms optimization technique was used to fine-tune these design parameters to improve the performance of the proposed controller. From the results, found that the quantitative comparison between the present study and the related articles previously published which used sliding mode observer control, showed reasonable agreement. To comparison between convolution Backstepping control and optimal Backstepping control with Bat algorithms, at the control action consumptions. It was found that the position error of the prosthetic knee is enhanced by 9% at joint1 and 7.4% at joint2, respectively. Therefore, the results are considered satisfactory for such biomedical systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".