Add figures for the control section

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Thomas Dehaeze 2020-04-29 10:11:10 +02:00
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@ -690,6 +690,8 @@ Then, using the model, we can
- include a multi-body model of the nano-hexapod and closed-loop simulations - include a multi-body model of the nano-hexapod and closed-loop simulations
** Wanted position of the sample and position error ** Wanted position of the sample and position error
<<sec:pos_error_nass>>
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. 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.
To do so, we need to perform several computations (summarized in Figure [[fig:control-schematic-nass]]): To do so, we need to perform several computations (summarized in Figure [[fig:control-schematic-nass]]):
@ -804,7 +806,7 @@ The sensibilities to ground motion in the Y and Z directions are shown in Figure
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. 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.
Thus, a stiff nano-hexapod is better for reducing the effect of ground motion at low frequency. Thus, a stiff nano-hexapod is better for reducing the effect of ground motion at low frequency.
It will be further suggested that using soft mounts for the granite can greatly lower the sensibility to ground motion. It will be suggested in Section [[sec:soft_granite]] that using soft mounts for the granite can greatly lower the sensibility to ground motion.
#+name: fig:opt_stiff_sensitivity_Dw #+name: fig:opt_stiff_sensitivity_Dw
#+caption: Sensitivity to Ground motion to the position error of the sample #+caption: Sensitivity to Ground motion to the position error of the sample
@ -998,44 +1000,139 @@ This show how the dynamics evolves with the stiffness and how different effects
** Conclusion ** Conclusion
#+begin_important #+begin_important
In Section [[sec:optimal_stiff_dist]], it has been concluded that a nano-hexapod stiffness 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.
Section [[sec:optimal_stiff_plant]] 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.
A stiffness of $10^5\,[N/m]$ will be used. 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.
Provided that the samples used have a first mode that is sufficiently high in frequency, the total plant dynamic uncertainty should be manageable.
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.
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]].
#+end_important #+end_important
#+begin_important
It is preferred that *one* controller is working for all the payloads.
If not possible, the alternative would be to develop an adaptive controller that depends on the payload mass/inertia.
#+end_important
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]].
* Robust Control Architecture * Robust Control Architecture
<<sec:robust_control_architecture>> <<sec:robust_control_architecture>>
** Introduction :ignore: ** Introduction :ignore:
https://tdehaeze.github.io/nass-simscape/optimal_stiffness_control.html https://tdehaeze.github.io/nass-simscape/optimal_stiffness_control.html
stiffness 10^5 stiffness 10^5
It is preferred that *one* controller is designed such that it will give acceptable performance for all the payloads that will be used.
This is quite challenging as the plant dynamics does depend quite a lot on the payload's mass.
It is difficult to design a
As there is a trade-off robustness/performance, the bigger the plant dynamic change, the lower the attainable performance.
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.
This would require to measure the mass/inertia of each used payload and
adaptive control is generally difficult to use in practice.
HAC-LAC
#+begin_quote
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:
- The active damping extends outside the bandwidth of the HAC and reduces the settling time of the modes which are outsite the bandwidth
- The active damping makes it easier to gain-stabilize the modes outside the bandwidth of the output loop (improved gain margin)
- The larger damping of the modes within the controller bandwidth makes them more robust to the parmetric uncertainty (improved phase margin)
#+end_quote
#+name: fig:control_architecture_hac_lac_one_input
#+caption: HAC-LAC Architecture with a system having only one input
[[file:figs/control_architecture_hac_lac_one_input.png]]
** Active Damping and Sensors to be included ** Active Damping and Sensors to be included
Ways to damp: Active Damping can help with two things
- Force Sensor
#+begin_quote
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.
Such low-gain controllers are often called Low Authority Controllers (LAC), because they modify the poles of the system only slightly.
#+end_quote
There are three main ways to actively damp a system:
- force Sensor
- Relative Velocity Sensors - Relative Velocity Sensors
- Inertial Sensor - Inertial Sensor
Because of the rotation
https://tdehaeze.github.io/rotating-frame/index.html https://tdehaeze.github.io/rotating-frame/index.html
Sensors to be included: Thus, relative motion sensors should be included in each of the nano-hexapod's leg.
The decentralized direct velocity feedback control architecture is shown in figure [[fig:control_architecture_dvf]] where:
- $\bm{\tau}$: Forces applied in each leg
- $\bm{\tau}_m$: Force sensor located in each leg
- $\bm{\mathcal{X}}$: Measurement of the payload position with respect to the granite
- $d\bm{\mathcal{L}}$: Measurement of the (small) relative motion of each leg
The controller $\bm{K}_{\text{DVF}}$ is a diagonal
#+name: fig:control_architecture_dvf
#+caption: Low Authority Control: Decentralized Direct Velocity Feedback
[[file:figs/control_architecture_dvf.png]]
#+name: fig:opt_stiff_primary_plant_damped_L
#+caption: Primary plant in the space of the legs with (dashed) and without (solid) Direct Velocity Feedback
[[file:figs/opt_stiff_primary_plant_damped_L.png]]
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.
This is probably not the optimal gain that could be used, and further analysis and optimization will be performed.
#+name: fig:opt_stiff_sensibility_dist_dvf
#+caption: Norm of the transfer function from vertical disturbances to vertical position error with (dashed) and without (solid) Direct Velocity Feedback applied
[[file:figs/opt_stiff_sensibility_dist_dvf.png]]
** Motion Control ** Motion Control
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.
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 $\bm{r}_\mathcal{X}$.
The computation done in such block was explained briefly in Section [[sec:pos_error_nass]].
From the position error express in the frame of the nano-hexapod, $\bm{J}$
$\bm{\epsilon}_\mathcal{L}$ thus express the length error of each of the nano hexapod's leg such that it position the sample at the correct position.
Then, a diagonal controller $\bm{K}_\mathcal{L}$ generates the required force in each leg such that
#+name: fig:control_architecture_hac_dvf_pos_L
#+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
[[file:figs/control_architecture_hac_dvf_pos_L.png]]
#+name: fig:opt_stiff_primary_plant_L
#+caption: Diagonal elements of the transfer function matrix from $\bm{\tau}^\prime$ to $\bm{\epsilon}_{\mathcal{X}_n}$ for the three considered masses
[[file:figs/opt_stiff_primary_plant_L.png]]
#+name: fig:opt_stiff_primary_loop_gain_L
#+caption: Loop gain for the primary plant
[[file:figs/opt_stiff_primary_loop_gain_L.png]]
#+name: fig:opt_stiff_primary_control_L_senbility_dist
#+caption: Sensibility to disturbances when the HAC-LAC control is applied
[[file:figs/opt_stiff_primary_control_L_senbility_dist.png]]
** Simulation of Tomography Experiments ** Simulation of Tomography Experiments
<<sec:tomography_experiment>> <<sec:tomography_experiment>>
The obtained performances for all the three considered masses are very similar.
That shows the robustness of the system.
#+name: fig:opt_stiff_hac_dvf_L_psd_disp_error #+name: fig:opt_stiff_hac_dvf_L_psd_disp_error
#+caption: Amplitude Spectral Density of the position error in Open Loop and with the HAC-LAC controller #+caption: Amplitude Spectral Density of the position error in Open Loop and with the HAC-LAC controller
[[file:figs/opt_stiff_hac_dvf_L_psd_disp_error.png]] [[file:figs/opt_stiff_hac_dvf_L_psd_disp_error.png]]
@ -1053,11 +1150,22 @@ Sensors to be included:
[[file:figs/closed_loop_sim_zoom.gif]] [[file:figs/closed_loop_sim_zoom.gif]]
** Conclusion ** Conclusion
* Further notes * Further notes
Soft granite <<sec:further_notes>>
** Using soft mounts for the
<<sec:soft_granite>>
#+name: fig:opt_stiff_soft_granite_Dw
#+caption: Change of sensibility to Ground motion when using a stiff Granite (solid curves) and a soft Granite (dashed curves)
[[file:figs/opt_stiff_soft_granite_Dw.png]]
This means that above the suspension mode of the granite (here around 2Hz), the granite
Sensible to detector motion? Sensible to detector motion?
** Others
Common metrology frame for the nano-focusing optics and the measurement of the sample position? Common metrology frame for the nano-focusing optics and the measurement of the sample position?
Cable forces? Cable forces?

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ref.bib
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@ -18,3 +18,15 @@
url = {https://doi.org/10.1541/ieejjia.7.127}, url = {https://doi.org/10.1541/ieejjia.7.127},
tags = {favorite}, tags = {favorite},
} }
@book{preumont18_vibrat_contr_activ_struc_fourt_edition,
author = {Andre Preumont},
title = {Vibration Control of Active Structures - Fourth Edition},
year = {2018},
publisher = {Springer International Publishing},
url = {https://doi.org/10.1007/978-3-319-72296-2},
doi = {10.1007/978-3-319-72296-2},
pages = {nil},
series = {Solid Mechanics and Its Applications},
tags = {favorite, parallel robot},
}