Bond graph modeling of a 3-joint string-tube actuated finger prosthesis

Mishra, Neeraj Kumar; Vaz, Anand

doi:10.1016/j.mechmachtheory.2017.06.018

Cited by 19 publications

(10 citation statements)

References 18 publications

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“…Other research activities rely on different types of modelling techniques, suc [49,50], where a bond graph mathematical model was used to describe the biome cal characteristics of the upper limbs tendons during grasping. In [51], bond graph used to describe the extensor mechanism of a finger, being represented as defo strings, and assumed to pass through hooks fixed at predetermined points on rigi Here, K sp and τ sp represent spindle gain and delay factor.…”

Section: Neuromuscular Dynamics Modelmentioning

confidence: 99%

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

Scibilia

Pedrocchi

Fortuna

2022

Sensors

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The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.

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Section: Neuromuscular Dynamics Modelmentioning

confidence: 99%

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

Scibilia

Pedrocchi

Fortuna

2022

Sensors

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“…A detailed multibond graph model [6] based on rigid body dynamics has been demonstrated for real and virtual phalange system as shown in Fig. 4.…”

Section: Multibond Graph Modelmentioning

confidence: 99%

Motion Control of a Phalange Using Tendon-Based Actuation System: A Bond Graph Approach

Uppal

Vaz

2021

Lecture Notes in Mechanical Engineering

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Daily interactions with the environment by the hands such as grasping, manipulating, writing, pull/push, carry, and playing music require specific forces and some precise movements provided by the various elements of the human hand. Skeletal muscles play a significant role in locomotion of upper as well as lower limbs. The muscle tendon unit involves nonlinear characteristics. A biomechanical model has been proposed to control the motion of a phalange, actuated through the muscle tendon unit by a virtual phalange, a part of the control by the central nervous system. The authors propose a bond graph based mathematical model to control the motion of a phalange through a virtual system using tendon actuation. Simulation results validate the proposed bond graph model.

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“…Mishra and Vaz used bond graph for the modeling of a 3-joint string tube actuated prosthesis. They used WBGOs for the modeling of different subsystems of a prosthetic finger mechanism and analyzed their dynamic behavior [6].…”

Section: Introductionmentioning

confidence: 99%

A Bond Graph Model for the Estimation of Torque Requirements at the Knee Joint During Sit-to-Stand and Stand-to-Sit Motions

Soni

Vaz

2021

Lecture Notes in Mechanical Engineering

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Sit-to-stand (SiTSt) and stand-to-sit (StTSi) are some of the frequent but crucial motions executed by the human body during daily life activities. During these motions, information of torque required at the knee joint is necessary for the development of knee assistance devices. In this work, a bond graph model has been developed to estimate torque requirements at the knee joint during SiTSt and StTSi motions. The bond graph offers graphical representation of a system, causality and power transactions between the subsystems, and algorithmic derivation of system equations. Word Bond Graph Objects (WBGOs) have been used for their compact representations of large systems. The model comprises two seven-link sub-models, one is virtual and the other is actual. The virtual sub-model is considered to be a part of the central nervous system (CNS). It generates the required joint angles for SiTSt and StTSi motions for desired movement trajectories. Based on these joint angles required, joint torques are provided to the actual sub-model through proportionalderivative (PD) controllers. Variations in the torque requirements have been estimated during SiTSt and StTSi motions, and the simulation results are presented.

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Bond graph modeling of a 3-joint string-tube actuated finger prosthesis

Cited by 19 publications

References 18 publications

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

Motion Control of a Phalange Using Tendon-Based Actuation System: A Bond Graph Approach

A Bond Graph Model for the Estimation of Torque Requirements at the Knee Joint During Sit-to-Stand and Stand-to-Sit Motions

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