An Extended Parameter Estimation Disturbance Observer for an Active Ankle Foot Orthosis
Bibliographic record
Abstract
An active ankle foot orthosis (AAFO) is an assistive device that applies plantarflexion and dorsiflexion assistance to the ankle joint by means of a compliant actuator. The device must apply sufficient torque assistance to track the desired ankle trajectory. However, torque disturbances are prevalent throughout the gait cycle. Accurately modelling the AAFO in conjunction with the ankle joint disturbance torque is a difficult task, as the model parameters can change over time. As a result, parameters such as inertia and friction are often roughly estimated based on the user’s weight. The uncertainties due to unmodelled disturbances and errors in dynamics modelling can severely compromise the device’s ability to provide appropriate assistance.This paper presents a novel extended parameter estimation observer combined with a disturbance rejection controller to estimate the model’s inertia and friction. First, an extended state observer (ESO) is employed in which the extended state is the estimated disturbance. Knowing the nominal ankle torque trajectory and the disturbance, a novel control law is formulated to reject the effects of endogenous disturbance torque during trajectory tracking. Then, based on the observed difference between the observed disturbance and nominal ankle torque, the paper introduces a novel method to estimate the inertial and friction parameters of the AAFO.Simulation results show that state feedback with the ESO is able to reduce the root mean square tracking error by 5.2% and 71.1% for high and low feedback gains, respectively. The results also indicate that the estimated AAFO and ankle joint inertial and damping parameters converge close to the nominal plant parameters. Simulations also show the effectiveness of the estimation laws from various initial plant estimates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".