首页|Data on Robotics Described by Researchers at University Mentouri of Constantine (Model-free Variable Impedance Control for Upper Limb Rehabilitation Robot)

Data on Robotics Described by Researchers at University Mentouri of Constantine (Model-free Variable Impedance Control for Upper Limb Rehabilitation Robot)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting originating in Constantine, Algeria, by NewsRx journalists, research stated, “This paper presents an innovative appr oach to control upper-limb rehabilitation robots for both passive and active-ass istive rehabilitation therapy. In contrast to conventional model-based impedance control strategies, which may compromise controller stability and robustness du e to model uncertainties, unmodeled dynamics, and external disturbances, our pro posed model-free impedance control (MFIC) strategy eliminates the requirement fo r prior knowledge about the controlled system dynamics.” The news reporters obtained a quote from the research from the University Mentou ri of Constantine, “MFIC is achieved by incorporating model-free control into co nventional impedance control, employing time delay estimation (TDE) to estimate unknown dynamics. Numerical simulations confirm that MFIC outperforms traditiona l impedance control in terms of tracking performance and robustness. Furthermore , model-free variable impedance control (MFVIC) is introduced by enhancing MFIC with online impedance parameters adaptation using fuzzy logic control. The desir ed impedance model adapts according to motion and contact torque measurements. M FVIC employs two fuzzy systems to adjust the desired impedance model for two sta ges of rehabilitation: passive and active-assistive rehabilitation training.”

ConstantineAlgeriaEmerging Technolog iesHealth and MedicineMachine LearningNano-robotRehabilitationRobotR oboticsUniversity Mentouri of Constantine

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.11)