Add figures for the control section
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figs/control_architecture_dvf.png
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figs/control_architecture_hac_dvf_pos_L.png
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figs/control_architecture_hac_lac_one_input.png
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figs/opt_stiff_primary_control_L_senbility_dist.png
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figs/opt_stiff_primary_loop_gain_L.png
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figs/opt_stiff_primary_plant_L.png
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figs/opt_stiff_primary_plant_damped_L.png
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figs/opt_stiff_sensibility_dist_dvf.png
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figs/opt_stiff_soft_granite_Dw.png
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index.org
@ -690,6 +690,8 @@ Then, using the model, we can
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- include a multi-body model of the nano-hexapod and closed-loop simulations
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** Wanted position of the sample and position error
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<<sec:pos_error_nass>>
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For the control of the nano-hexapod, we need to now the sample position error (the motion to be compensated) in the frame of the nano-hexapod.
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To do so, we need to perform several computations (summarized in Figure [[fig:control-schematic-nass]]):
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@ -804,7 +806,7 @@ The sensibilities to ground motion in the Y and Z directions are shown in Figure
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We can see that above the suspension mode of the nano-hexapod, the norm of the transmissibility is close to one until the suspension mode of the granite.
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Thus, a stiff nano-hexapod is better for reducing the effect of ground motion at low frequency.
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It will be further suggested that using soft mounts for the granite can greatly lower the sensibility to ground motion.
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It will be suggested in Section [[sec:soft_granite]] that using soft mounts for the granite can greatly lower the sensibility to ground motion.
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#+name: fig:opt_stiff_sensitivity_Dw
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#+caption: Sensitivity to Ground motion to the position error of the sample
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@ -998,44 +1000,139 @@ This show how the dynamics evolves with the stiffness and how different effects
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** Conclusion
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#+begin_important
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In Section [[sec:optimal_stiff_dist]], it has been concluded that a nano-hexapod stiffness
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Section [[sec:optimal_stiff_plant]]
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In Section [[sec:optimal_stiff_dist]], it has been concluded that a nano-hexapod stiffness below $10^5-10^6\,[N/m]$ helps reducing the high frequency vibrations induced by all sources of disturbances considered.
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As the high frequency vibrations are the most difficult to compensate for when using feedback control, a soft hexapod will most certainly helps improving the performances.
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A stiffness of $10^5\,[N/m]$ will be used.
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In Section [[sec:optimal_stiff_plant]], we concluded that a nano-hexapod leg stiffness in the range $10^5 - 10^6\,[N/m]$ is a good compromise between the uncertainty induced by the micro-station dynamics and by the rotating speed.
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Provided that the samples used have a first mode that is sufficiently high in frequency, the total plant dynamic uncertainty should be manageable.
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Thus, a stiffness of $10^5\,[N/m]$ will be used in Section [[sec:robust_control_architecture]] to develop the robust control architecture and to perform simulations.
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A more detailed study of the determination of the optimal stiffness based on all the effects is available [[https://tdehaeze.github.io/nass-simscape/uncertainty_optimal_stiffness.html][here]].
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#+end_important
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#+begin_important
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It is preferred that *one* controller is working for all the payloads.
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If not possible, the alternative would be to develop an adaptive controller that depends on the payload mass/inertia.
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#+end_important
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A more detailed study of the determination of the optimal stiffness based on all the effects is available [[https://tdehaeze.github.io/nass-simscape/uncertainty_optimal_stiffness.html][here]].
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* Robust Control Architecture
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<<sec:robust_control_architecture>>
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** Introduction :ignore:
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https://tdehaeze.github.io/nass-simscape/optimal_stiffness_control.html
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stiffness 10^5
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It is preferred that *one* controller is designed such that it will give acceptable performance for all the payloads that will be used.
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This is quite challenging as the plant dynamics does depend quite a lot on the payload's mass.
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It is difficult to design a
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As there is a trade-off robustness/performance, the bigger the plant dynamic change, the lower the attainable performance.
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If not possible to develop a robust controller that gives acceptable performance, an alternative would be to develop an *adaptive* controller that depends on the payload mass/inertia.
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This would require to measure the mass/inertia of each used payload and
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adaptive control is generally difficult to use in practice.
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HAC-LAC
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#+begin_quote
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The HAC/LAC approach consist of combining the two approached in a dual-loop control as shown in Figure [[fig:control_architecture_hac_lac_one_input]]. The inner loop uses a set of collocated actuator/sensor pairs for decentralized active damping with guaranteed stability ; the outer loop consists of a non-collocated HAC based on a model of the actively damped structure. This approach has the following advantages:
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- The active damping extends outside the bandwidth of the HAC and reduces the settling time of the modes which are outsite the bandwidth
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- The active damping makes it easier to gain-stabilize the modes outside the bandwidth of the output loop (improved gain margin)
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- The larger damping of the modes within the controller bandwidth makes them more robust to the parmetric uncertainty (improved phase margin)
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#+end_quote
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#+name: fig:control_architecture_hac_lac_one_input
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#+caption: HAC-LAC Architecture with a system having only one input
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[[file:figs/control_architecture_hac_lac_one_input.png]]
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** Active Damping and Sensors to be included
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Ways to damp:
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- Force Sensor
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Active Damping can help with two things
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#+begin_quote
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Active damping is very effective in reducing the settling time of transient disturbances and the effect of steady state disturbances near the resonance frequencies of the system; however, away from the resonances, the active damping is completely ineffective and leaves the closed-loop response essentially unchanged.
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Such low-gain controllers are often called Low Authority Controllers (LAC), because they modify the poles of the system only slightly.
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#+end_quote
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There are three main ways to actively damp a system:
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- force Sensor
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- Relative Velocity Sensors
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- Inertial Sensor
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Because of the rotation
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https://tdehaeze.github.io/rotating-frame/index.html
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Sensors to be included:
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Thus, relative motion sensors should be included in each of the nano-hexapod's leg.
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The decentralized direct velocity feedback control architecture is shown in figure [[fig:control_architecture_dvf]] where:
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- $\bm{\tau}$: Forces applied in each leg
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- $\bm{\tau}_m$: Force sensor located in each leg
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- $\bm{\mathcal{X}}$: Measurement of the payload position with respect to the granite
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- $d\bm{\mathcal{L}}$: Measurement of the (small) relative motion of each leg
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The controller $\bm{K}_{\text{DVF}}$ is a diagonal
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#+name: fig:control_architecture_dvf
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#+caption: Low Authority Control: Decentralized Direct Velocity Feedback
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[[file:figs/control_architecture_dvf.png]]
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#+name: fig:opt_stiff_primary_plant_damped_L
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#+caption: Primary plant in the space of the legs with (dashed) and without (solid) Direct Velocity Feedback
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[[file:figs/opt_stiff_primary_plant_damped_L.png]]
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As shown in Figure [[fig:opt_stiff_sensibility_dist_dvf]], the use of the DVF control lowers the sensibility to disturbances in the vicinity of the nano-hexapod resonance but increases the sensibility at higher frequencies.
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This is probably not the optimal gain that could be used, and further analysis and optimization will be performed.
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#+name: fig:opt_stiff_sensibility_dist_dvf
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#+caption: Norm of the transfer function from vertical disturbances to vertical position error with (dashed) and without (solid) Direct Velocity Feedback applied
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[[file:figs/opt_stiff_sensibility_dist_dvf.png]]
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** Motion Control
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The complete control architecture is shown in Figure [[fig:control_architecture_hac_dvf_pos_L]] where an outer loop is added to the decentralized direct velocity feedback loop.
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The block =Compute Position Error= is used to compute the position error of the sample with respect to the nano-hexapod's base platform $\bm{\epsilon}_{\mathcal{X}_n}$ from the actual measurement of the sample's pose $\bm{\mathcal{X}}$ and the wanted pose $\bm{r}_\mathcal{X}$.
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The computation done in such block was explained briefly in Section [[sec:pos_error_nass]].
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From the position error express in the frame of the nano-hexapod, $\bm{J}$
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$\bm{\epsilon}_\mathcal{L}$ thus express the length error of each of the nano hexapod's leg such that it position the sample at the correct position.
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Then, a diagonal controller $\bm{K}_\mathcal{L}$ generates the required force in each leg such that
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#+name: fig:control_architecture_hac_dvf_pos_L
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#+caption: Cascade Control Architecture. The inner loop consist of a decentralized Direct Velocity Feedback. The outer loop consist of position control in the leg's space
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[[file:figs/control_architecture_hac_dvf_pos_L.png]]
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#+name: fig:opt_stiff_primary_plant_L
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#+caption: Diagonal elements of the transfer function matrix from $\bm{\tau}^\prime$ to $\bm{\epsilon}_{\mathcal{X}_n}$ for the three considered masses
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[[file:figs/opt_stiff_primary_plant_L.png]]
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#+name: fig:opt_stiff_primary_loop_gain_L
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#+caption: Loop gain for the primary plant
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[[file:figs/opt_stiff_primary_loop_gain_L.png]]
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#+name: fig:opt_stiff_primary_control_L_senbility_dist
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#+caption: Sensibility to disturbances when the HAC-LAC control is applied
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[[file:figs/opt_stiff_primary_control_L_senbility_dist.png]]
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** Simulation of Tomography Experiments
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<<sec:tomography_experiment>>
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The obtained performances for all the three considered masses are very similar.
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That shows the robustness of the system.
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#+name: fig:opt_stiff_hac_dvf_L_psd_disp_error
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#+caption: Amplitude Spectral Density of the position error in Open Loop and with the HAC-LAC controller
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[[file:figs/opt_stiff_hac_dvf_L_psd_disp_error.png]]
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@ -1053,11 +1150,22 @@ Sensors to be included:
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[[file:figs/closed_loop_sim_zoom.gif]]
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** Conclusion
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* Further notes
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Soft granite
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<<sec:further_notes>>
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** Using soft mounts for the
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<<sec:soft_granite>>
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#+name: fig:opt_stiff_soft_granite_Dw
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#+caption: Change of sensibility to Ground motion when using a stiff Granite (solid curves) and a soft Granite (dashed curves)
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[[file:figs/opt_stiff_soft_granite_Dw.png]]
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This means that above the suspension mode of the granite (here around 2Hz), the granite
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Sensible to detector motion?
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** Others
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Common metrology frame for the nano-focusing optics and the measurement of the sample position?
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Cable forces?
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12
ref.bib
@ -18,3 +18,15 @@
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url = {https://doi.org/10.1541/ieejjia.7.127},
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tags = {favorite},
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}
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@book{preumont18_vibrat_contr_activ_struc_fourt_edition,
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author = {Andre Preumont},
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title = {Vibration Control of Active Structures - Fourth Edition},
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year = {2018},
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publisher = {Springer International Publishing},
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url = {https://doi.org/10.1007/978-3-319-72296-2},
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doi = {10.1007/978-3-319-72296-2},
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pages = {nil},
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series = {Solid Mechanics and Its Applications},
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tags = {favorite, parallel robot},
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}
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