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biomechanik:modellierung:mm4:mc2

MATLAB Codes: FMCH in MATLAB


Module-Icon MC2 FMCH model
Event none
Author Maziar A. Sharbafi
Requirements Module EM, EMSLIP and MC1
teaching time 45 min
MATLAB code TSLIP_FMCH_Model
Last modified 11.7.2017
Force modulated compliant hip (FMCH)

The key idea behind the FMCH approach is to substitute the VPP concept based on the idea of a virtual pendulum (VP) with a structural model. Therefore, the hip joint (between trunk and leg) of the T-SLIP model was equipped with a passive spring (Fig. 1) simulating the effects of extensor and flexor muscles resulting in stable walking (Rummel & Seyfarth 2010).


Figure 1. Compliant hip for implementing VPP concept.

Considering two hip springs are in line with human muscular system as shown in Fig. 2. Monoarticular (GL, Gloteus or PL, ploteus) or biarticular (Ham, Hamstrings or RF, Rectus Femoris) can be considered as compliant hip muscles for posture control that can be mimicked by this model. It can be analytically shown that the springs in the proposed model better predict the biarticular muscles than the monoarticular ones [Sharbafi 2016]. The reasononing is based on the definition of the virtual leg between the hip and the foot which is different from thigh (from hip to knee).


Figure 2. The concept of compliant hip and human muscular system

With this model stable hopping/running [Sharbafi 2013] and walking [Rummel 2008] can be generated. Such a passive model is even robust against perturbations. The results of hip torque generation and the comparison with VPPC are shown in Fig. 3. In this figure, two nonlinear spring relationship are considered to approach the hip torque of VPP which was not successful.


Figure 3. The comparison of VPPC and compliant hip model regarding the hip torque in running from [Sharbafi2013].

Since the hip torque patterns are different from human hip torques developed by VPP (see, Fig. 3), we need to find a control signal to adjust the compliance. For this, it is useful to look at the muscle models and neuromuscular control in brief. Fig. 4 shows Hill-type muscle model, and two neural control using the reflex signals (muscle length, speed, and force) to adjust the muscle stiffness and damping in [Geyer 2003] and [Geunter 2010], respectively. In Geyer model, the positive force feedback yields in stable hopping for a two segmented leg. Inspired from this model, we have developed the FMCH (force modulated compliant hip) model in which the leg force is employed to tune the hip compliance as a reflex pathway.


Figure 4. Muscle modelling and neuromusclar control in locomotion.


Figure 5. FMCH model for posture control

As can be seen in Fig. 5, the FMCH model is very simple linear relation between the leg force and hum muscle stiffness. A large improvement in balance control was achieved by applying the leg force to modulate hip compliance. It results in human-like posture balance and provides a mechanical explanation for the VPP concept (Sharbafi & Seyfarth 2014, 2015). In this force modulated compliant hip (FMCH) model the hip torque (τ) is a product of a constant ($c$), the leg force (F_s) and the difference between the hip to the leg angle (ψ) and its rest angle (ψ_0) as follows:

$\tau=cF_s(\psi-\psi_0)$

It is mathematically shown that the required torque in VPPC is precisely approximated by FMCH in a range of hip and leg movements representative for human gaits (Sharbafi & Seyfarth 2015). The relation between the VPP point and the rest angle and normalized stiffness of the FMCH model are described in Fig. 6.


Figure 6. Relation between FMCH and VPP.

Fig. 7 shows the leg force and hip torque developed in FMCH model for stable walking at normal walking speed (1.4 m/s) compared to the human experimental results. In addition to explain human gaits, this concept can be utilized in bipedal robot control and assistive devices (e.g., exoskeleton). This is explained in Applications.


Figure 7. FMCH for walking at normal speed.

In Fig. 8, hip torque and angle generated by FMCH is compared with the VPPC controller in running. It is shown that the main drawback of passive compliant hip is now resolved.In addition, a clear VPP point can be obtained as shown in Fig. 9 and 10. In Fig. 9, we targetted a VPP at 8.5cm above CoM fo running and used the formulation presented in Fig. 6. It is observed that the approximation is very precise and result in VPP point at the same place. This value is different for the passive compliant hip models. Fig. 10 shows the results of implementing FMCH for walking at different speeds.


Figure 8. Comparison of VPP, compliant hip with nonlinear compliance and FMCH.


Figure 9. VPP, resulted from passive compliant hip and FMCH in running.

Figure 10. FMCH for generating VPP in walking at different speeds.

This model can be considered as a candidate for neuro-mechanical template for posture control. The model suggests a sensory pathway originating at a force sensor of the leg extensor muscle (e.g. in the knee) and a gain factor (constant c). In contrast to the neural system, no processing delays are considered in the FMCH model. Also, the muscle function is reduced to an activation-dependent tunable spring. These are clear simplifications compared to neuro-muscular processing of sensory data. For all of these reasons we call the FMCH model an extended template model and not an anchor model.

Exercise:

  1. Prove the relation between FMCH and VPP shown in Fig. 6.
  2. What are the drawbacks of the passive compliant hip model without force feedback? Has the hip torque at touchdown a physical meaning? What is wrong at this moment?
  3. Why are biarticular springs better representatives for the hip springs with TSLIP model and not the monoarticular ones?

References

Geyer, H., Seyfarth, A., & Blickhan, R. (2003). Positive force feedback in bouncing gaits?. Proceedings of the Royal Society of London B: Biological Sciences, 270(1529), 2173-2183.

Geyer, H., & Herr, H. (2010). A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Transactions on neural systems and rehabilitation engineering, 18(3), 263-273.

Günther, M., & Schmitt, S. (2010). A macroscopic ansatz to deduce the Hill relation. Journal of theoretical biology, 263(4), 407-418.

Maus, H. M., Lipfert, S. W., Gross, M., Rummel, J., & Seyfarth, A. (2010). Upright human gait did not provide a major mechanical challenge for our ancestors. Nature Communications, 1, 70.

Rummel, J., & Seyfarth, A. (2010) “Passive stabilization of the trunk in walking,” in International Conference on Simulation, Modeling and Programming for Autonomous Robots, (SIMPAR).

Sharbafi, M. A., Maufroy, C., Ahmadabadi, M. N., Yazdanpanah, M. J., & Seyfarth, A. (2013a). Robust hopping based on virtual pendulum posture control. Bioinspiration & biomimetics, 8(3), 036002.

Sharbafi, M. A., Ahmadabadi, M. N., Yazdanpanah, M. J., Mohammadinejad, A., & Seyfarth, A., (2013b) “Compliant hip function simplifies control for hopping and running,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Sharbafi, M. A., & Seyfarth, A. (2014). Stable running by leg force-modulated hip stiffness. In 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 204-210.

Sharbafi, M. A., & Seyfarth, A. (2015). FMCH: A new model for human-like postural control in walking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5742-5747.

Sharbafi, M.A., Rode, C., Kurowski, S., Scholz, D., Möckel, R., Radkhah, K., Zhao, G., Mohammadi, A., von Stryk, O. and Seyfarth, A., 2016. A new biarticular actuator design facilitates control of leg function in BioBiped3. Bioinspiration & Biomimetics, 11(4), p.046003.

biomechanik/modellierung/mm4/mc2.txt · Zuletzt geändert: 28.11.2022 00:58 von 127.0.0.1


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