Design and Control of a Multifunctional Ankle Exoskeleton Powered by Magnetorheological Actuators to Assist Walking, Jumping, and Landing
Bibliographic record
Abstract
Lower-limb exoskeletons have shown increasing potential to augment human performance in many locomotion tasks. However, most lower-limb exoskeletons use highly geared, nonback-drivable actuators with limited power and force bandwidth in order to be light enough to be carried without metabolic penalty. Moreover, they rely on controllers that depend on past motion history to assist the user, which limits the multifunctional capabilities of exoskeletons. Here, we study the potential of delocalized magnetorheological (MR) clutches to provide transparent but yet powerful multifunctional exoskeleton assistance. A single high-speed, lightweight motor is coupled with two MR clutches that modulate the plantar-flexion torque at each ankle. The exoskeleton is controlled by a state map controller that can assist users in real time while walking, jumping, and landing. Results confirm the potential of the MR actuation approach by demonstrating instantaneous adaptation to transient walking and by producing a maximal torque of 90 N·m per ankle with a total power of 1.4 kW when jumping. The system also actively braked landing impact and achieved multifunctional assistance in a sequence of walking, jumping, and landing. With a total mass of 6.2 kg including 0.9 kg on each leg, the system reduces metabolic cost of walking by 5.6% on average with tethered electronics and power supply.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".