Bond Graph Modeling and Kalman Filter Observer Design for an Industrial Back-Support Exoskeleton

Barjuei, Erfan Shojaei; Caldwell, Darwin G.; Ortiz, Jesús

doi:10.3390/designs4040053

Cited by 15 publications

(8 citation statements)

References 29 publications

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“…Limited research is available on modeling a musculoskeletal biomechanical model, particularly the human hand, that utilizes the bond graph technique. A few references that can be found in the literature of a biomechanical model are Mughal and Iqbal (2013), Mishra and Vaz (2017), and Shojaei Barjuei et al (2020).…”

Section: Bond Graph Model Formulation Of the Physiological Elementsmentioning

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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Section: Bond Graph Model Formulation Of the Physiological Elementsmentioning

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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“…Hence, the bond graph technique was used in this study for the modeling of exoskeleton for rehabilitation. Similar studies in which a bond graph is used to model a powered exoskeleton for use as an industrial backpack and a knee joint exoskeleton are presented in [17,18].…”

Section: Introductionmentioning

confidence: 99%

Design of Model-Based and Model-Free Robust Control Strategies for Lower Limb Rehabilitation Exoskeletons

et al. 2022

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Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.

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“…Within the mechanical systems that constitute the core of robotic rehabilitation systems, two main types can be distinguished: open kinematic chains, mainly exoskeletons [28][29][30], and closed kinematic chains, mainly parallel robots. One of the main differences between exoskeletons [31] and parallel robots for knee rehabilitation (RPRs) [32] is that the RPRs exert mechanical actions on the distal end of the limb.…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Musculoskeletal Model of the Lower Limb for the Control of Rehabilitation Robots: Assumptions and Validation

et al. 2022

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We present and validate a computationally efficient lower limb musculoskeletal model for the control of a rehabilitation robot. It is a parametric model that allows the customization of joint kinematics, and it is able to operate in real time. Methods: Since the rehabilitation exercises corresponds to low-speed movements, a quasi-static model can be assumed, and then muscle force coefficients are position dependent. This enables their calculation in an offline stage. In addition, the concept of a single functional degree of freedom is used to minimize drastically the workspace of the stored coefficients. Finally, we have developed a force calculation process based on Lagrange multipliers that provides a closed-form solution; in this way, the problem of dynamic indeterminacy is solved without the need to use an iterative process. Results: The model has been validated by comparing muscle forces estimated by the model with the corresponding electromyography (EMG) values using squat exercise, in which the Spearman’s correlation coefficient is higher than 0.93. Its computational time is lower than 2.5 ms in a conventional computer using MATLAB. Conclusions: This procedure presents a good agreement with the experimental values of the forces, and it can be integrated into real time control systems.

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Bond Graph Modeling and Kalman Filter Observer Design for an Industrial Back-Support Exoskeleton

Cited by 15 publications

References 29 publications

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

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

Design of Model-Based and Model-Free Robust Control Strategies for Lower Limb Rehabilitation Exoskeletons

A Computationally Efficient Musculoskeletal Model of the Lower Limb for the Control of Rehabilitation Robots: Assumptions and Validation

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