Development of trajectory and force controllers for 3-joint string-tube actuated finger prosthesis based on bond graph modeling

Mishra, Neeraj Kumar; Vaz, Anand

doi:10.1016/j.mechmachtheory.2019.103719

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…A model of a ball dropped in a bowl with a cartilage layer as a multibond graph is presented in [16]. Trajectory and force control schemes applied to a prosthetic finger mechanism using a multibond graph are presented in [17].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Physical Systems Formed by Bond Graphs and Multibond Graphs

Gonzalez-Avalos,

Gallegos,

Ayala-Jaimes

et al. 2023

Symmetry

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Current physical systems are built in more that one coordinate: for example, electrical power systems, aeronautical systems and robotic systems can be modeled in multibond graphs (MBG). However, in these systems, some elements use only one axis or dimension—for example, actuators and controllers—which can be modeled in bond graphs (BG). Therefore, in this paper, modeling of systems in multibond graphs and bond graphs (MBG-BG) is presented. Likewise, the junction structure of systems represented by (MBG-BG) is introduced. From this structure, mathematical modeling in the state space is presented. Likewise, modeling of systems on a platform (MBG-BG) can be seen as symmetric to the mathematical model that represents these systems. Finally, a synchronous generator modeled by (MBG-BG) as a case study is developed, and simulation results using 20-Sim software are shown. Furthermore, an electrical power system connected to the power supply of a DC motor as another case study is explained.

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Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Physical Systems Formed by Bond Graphs and Multibond Graphs

Gonzalez-Avalos,

Gallegos,

Ayala-Jaimes

et al. 2023

Symmetry

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“…The researchers developed an embedded controller as a trade-off between the flexibility and size. Mishra and Vaz (2020) developed a three-joint string-tube-actuated finger bond graph model. In addition, the authors have suggested a force control method to track the intended force profile.…”

Section: Introductionmentioning

confidence: 99%

Bond graph modeling with linear quadratic integral control synthesis of a robotic digit in a human impaired hand for anthropomorphic coordination

Iqbal

Imtiaz

Mahmood

2022

Transactions of the Institute of Measurement and Control

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The movement coordination of the robotic digit(s) with the central nervous system (CNS) and the natural digit(s) is a complex task that needs to be executed successfully in an anthropomorphic hand. The task is challenging to resolve because of the CNS. We developed a theoretical framework for the biomechanical model of a partially impaired human hand utilizing the bond graph modeling technique by incorporating inertia, muscle, and visco-elastic dynamics. The research presents a partially impaired human hand model with a robotic digit and four natural digits having 21 degrees of freedom. We formulated a linear quadratic Gaussian (LQG) integral control technique for the 21st-order model to regulate the flexion and extension movement of the robotic digit while considering the disturbances. We have simulated the modeling scheme in MATLAB/Simulink. The flexion and extension movement and the angular velocity of the robotic finger are shown to be following all the physiological constraints of a natural finger. The settling time is achieved at 1.6 seconds, with a maximum flexion angle of 0.135 rad. The sensitivity analysis shows that the model is robust against disturbances. The simulation results exhibit the application of this scheme toward upper limb rehabilitation and improvement in prosthetic and exoskeleton designs.

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“…As a main mechanical interaction between humans and the environment, muscle force during movements is essential in daily life [1,2], such as intelligent prosthesis [3] and games [4]. However, it is impractical and inconvenient to use force sensors in many applications.…”

Section: Introductionmentioning

confidence: 99%

A novel sEMG-based force estimation method using deep-learning algorithm

Hua

Wang

2021

Complex Intell. Syst.

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This paper discusses the problem of force estimation represented by surface electromyography (sEMG) signals collected from an armband-like collection device. The scheme is proposed for the sake of two dimensions of sEMG signals: spatial and temporal information. From the point of space, first, appropriate channel number across all subjects is investigated. During this progress, an electrode channel selection method based on Spearman’s rank order correlation coefficient is utilized to detect signals from active muscle. Then, to reduce the computation and highlight the channel information, linear regression (LR) algorithm is conducted to weight each channel. Besides, the recurrent neural network (RNN) is used to capture the temporal information and model the relation between sEMG and output force. Experiments conducted on four subjects demonstrate that six channels are enough to characterize the muscle activity. By combining the selected channels with different weight coefficients, LR algorithm can fit the output force better than simply averaging them. Furthermore, RNN with long short-term memory cell shows the superiority in time series modeling, which can improve our results to a greater degree. Experimental results prove the feasibility of the proposed method.

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Development of trajectory and force controllers for 3-joint string-tube actuated finger prosthesis based on bond graph modeling

Cited by 9 publications

References 26 publications

Modeling and Simulation of Physical Systems Formed by Bond Graphs and Multibond Graphs

Modeling and Simulation of Physical Systems Formed by Bond Graphs and Multibond Graphs

Bond graph modeling with linear quadratic integral control synthesis of a robotic digit in a human impaired hand for anthropomorphic coordination

A novel sEMG-based force estimation method using deep-learning algorithm

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