ArticleJournal of computational and nonlinear dynamics2019
Model Predictive Control of a Feedback-Linearized Hybrid Neuroprosthetic System With a Barrier Penalty.
Article in Journal of computational and nonlinear dynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04453943 (Modeling, Optimization, and Control Methods for a Personalized Hybrid Walking Exoskeleton), which is not on this map. Cited by 4 papers.
What it found
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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.
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.
Modeling, Optimization, and Control Methods for a Personalized Hybrid Walking Exoskeleton
Who cites it
4 citing papers in PubMed.
- Nonlinear model predictive control of a conductance-based neuron model via data-driven forecasting.Journal of neural engineering · 2024Article
- Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning.Frontiers in robotics and AI · 2021Article
- Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton - Preliminary Results.IEEE transactions on medical robotics and bionics · 2020Article
- Sub-optimally Solving Actuator Redundancy in a Hybrid Neuroprosthetic System with a Multi-layer Neural Network Structure.International journal of intelligent robotics and applications · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function in individuals with neurological impairments. However, the rapid onset of FES-induced muscle fatigue significantly limits its duration of use and limb movement quality. In this paper, an electric motor-assist is proposed to alleviate the fatigue effects by sharing work load with FES. A model predictive control (MPC) method is used to allocate control inputs to FES and the electric motor. To reduce the computational load, the dynamics is feedback linearized so that the nominal model inside the MPC method becomes linear. The state variables: the angular position and the muscle fatigue are still preserved in the transformed state space to keep the optimization meaningful. Because after feedback linearization the original linear input constraints may become nonlinear and state-dependent, a barrier cost function is used to overcome this issue. The simulation results show a satisfactory control performance and a reduction in the computation due to the linearization.
Identifiers
What OpenQuestion holds
Registered trials
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.