Evidence map›Paper›PMID 41977908›Full record

ArticleSensors (Basel, Switzerland)2026

Dynamic Modelling and Control Strategy Analysis of a Lower-Limb Exoskeleton.

Huanrong Xiao, Teng Ran, Afang Jin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Huanrong XiaoCollege of Mechanical Engineering, Xinjiang University, Ürümqi 830047, China.
Teng RanCollege of Mechanical Engineering, Xinjiang University, Ürümqi 830047, China.
Afang JinCollege of Mechanical Engineering, Xinjiang University, Ürümqi 830047, China.

Funding

National Natural Science Foundation of China 52365039
6 · The paper itself

Abstract

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit-Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies-PD control, PD with gravity compensation, and the computed torque method-are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4-1.45 m/s, validating the effectiveness of the proposed model and control strategies.

Indexed as

Exoskeleton DeviceLower ExtremityBiomechanical PhenomenaGaitHumansModels, BiologicalMotion CaptureRoboticsTorqueWalkingcomputed torque methoddynamics modellingLagrangian equationlower-limb exoskeletontrajectory tracking control

Identifiers

PMID41977908
PMCPMC13074935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.