Adaptive Control for End-Effector Exoskeleton Robots With Unknown Dynamics and Actuator Parameters
Bibliographic record
Abstract
Exoskeleton robots hold immense promise in rehabilitation, serving as crucial aids for patient mobility and exercise. However, harnessing their capabilities requires overcoming significant control challenges arising from complex nonlinear dynamics and uncertainties in both models and actuators. This article introduces a novel adaptive backstepping controller designed specifically for exoskeleton robots navigating uncertain dynamics and actuator parameters. Unlike conventional approaches that rely on basis functions, the proposed controller (PL) integrates a modified function approximation technique (MFAT) to approximate dynamic parameters without the need for such functions. The MFAT effectively manages mismatched perturbations, while the backstepping control compensates for uncertainties associated with state variables, enhancing resilience to disturbances, especially in scenarios where the exoskeleton’s dynamics model is unknown. The Lyapunov stability analysis ensures uniformly ultimately bounded (UUB) signals within the closed-loop system. A comparative study conducted on the industrial robot IRB 120 validates the effectiveness of the PL and highlights its superior performance. Real-time implementation on a seven degrees of freedom (DOFs) wearable robot named ETS-MARSE confirms the efficiency of the control algorithm. The results from simulations and experiments underscore the efficacy of the proposed approach. The insights gained from this article pave the way to unlocking the full potential of exoskeleton robots in rehabilitation settings, promising improved patient outcomes and advancing human-technology interaction to new heights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".