Comprehensive Modeling and Simulation of an Anthropomorphic Robotic Exoskeleton for Rehabilitation

Saeed, Muhammad; Qin, Shiyin

doi:10.1109/ibcast.2019.8667197

Cited by 2 publications

(4 citation statements)

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“…The mathematical model of the system was developed using the bond graph modeling technique. This research work is an extension of our previous work presented in [13,46]. Previously, the designed MB control law provided disturbance rejection and uncertainty compensation but the final value in case of step and impulse inputs was not achieved.…”

Section: Model-based Robust Control Designmentioning

confidence: 88%

“…A schematic diagram of the exoskeleton and the bond graph model of the system generated using the 20-sim software are presented in Figures 2 and 3, respectively. For the control of link_1, associated with the hip joint, a composite model is required because, during the locomotion assistance and gait training, the motor at the hip joint (mhj), including the inertial mass, stiffness, and viscous friction associated with link_1, experiences the effect of the remaining structure, i.e., two motors (the motor at the knee joint (mkj) and the motor at the ankle joint (maj), two links, and the human leg except the thigh, as a mass-spring-damper load [13,46]. Similarly, the actuator at the knee joint is influenced by the third motor and third link in the form of a mass-spring-damper load.…”

Section: Mathematical Model Using Bond Graphmentioning

confidence: 99%

“…A composite model obtained as the mathematical model to control the position of link_1 is presented in Figure 4. The description of variables of the bond graph [13] of the composite model, and the transfer function of the composite model defining a relationship between the applied input voltage (E) and the link velocity (V) of link_1 with the combined effect at the output, are presented in Table 1 and Equation (3), respectively. State space realization is presented in Equation (4).…”

Section: Mathematical Model Using Bond Graphmentioning

confidence: 99%

“…An alternate approach for modeling the manipulators is the bond graph. The bond graph technique provides a model of the system with less complexity and more flexibility [13,14], as compared to the conventional Lagrangian method [15,16]. Hence, the bond graph technique was used in this study for the modeling of exoskeleton for rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Model-based Robust Control Designmentioning

confidence: 88%

Section: Mathematical Model Using Bond Graphmentioning

confidence: 99%

Section: Mathematical Model Using Bond Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations