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

Uppal, Sandeep Kumar; Vaz, Anand

doi:10.1007/978-981-16-0550-5_141

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Since in vivo and in vitro models of muscle tendons are difficult to study, a BG model has been developed by Uppal & Vaz [82,83] to study the movements of phalanges caused by muscle tendons. The model uses a virtual phalange to move with the real phalange and act as the central nervous system to control and actuate the corresponding muscle activation force.…”

Section: Bg Modelling In Biology and Physiologymentioning

confidence: 99%

A review of the diverse applications of bond graphs in biology and physiology

Akbarpour Ghazani,

Pan,

Tran

et al. 2024

Proc. R. Soc. A.

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Computational biology and physiology is an interdisciplinary endeavour, requiring input from biologists, physiologists, mathematicians, chemists, engineers and clinicians. These systems are composed of complex phenomena across disparate temporal and spatial scales, and a holistic understanding of system behaviour typically requires the application of advanced multi-scale models. While many modelling techniques have been used, the bond graph (BG) is the only approach for modelling physical systems, where ‘causality’ is represented graphically. Additionally, the BG approach with its intrinsic properties allows the modular construction of models and verifying the conservation of mass and energy algorithmically. The BG approach has been widely used in engineering and, more recently, has been increasingly applied to biology and physiological systems. In this review, we briefly introduce the concepts and strengths of BG modelling. Following this, we review the history of BGs in modelling cellular mechanisms, biochemical reactions and musculoskeletal and cardiovascular systems. Then, current developments in BG software are reviewed, and opportunities and perspectives on the future application of BGs are discussed.

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Section: Bg Modelling In Biology and Physiologymentioning

confidence: 99%

A review of the diverse applications of bond graphs in biology and physiology

Akbarpour Ghazani,

Pan,

Tran

et al. 2024

Proc. R. Soc. A.

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

Cited by 2 publications

References 5 publications

A review of the diverse applications of bond graphs in biology and physiology

A review of the diverse applications of bond graphs in biology and physiology

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

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