diff --git a/active_damping/index.html b/active_damping/index.html index 71b9f98..6611a2f 100644 --- a/active_damping/index.html +++ b/active_damping/index.html @@ -3,7 +3,7 @@ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
- +-First, in section 1, we will looked at the undamped system. +First, in section 1, we will looked at the undamped system.
Then, we will compare three active damping techniques:
@@ -371,19 +375,26 @@ The disturbances are:
+All the files (data and Matlab scripts) are accessible here. +
+ +We first look at the undamped system. The performance of this undamped system will be compared with the damped system using various techniques.
We initialize all the stages with the default parameters. @@ -391,7 +402,8 @@ The nano-hexapod is a piezoelectric hexapod and the sample has a mass of 50kg.
initializeGround(); +initializeInputs(); +initializeGround(); initializeGranite(); initializeTy(); initializeRy(); @@ -421,8 +433,8 @@ save( -1.2 Identification
++-1.2 Identification
-We identify the various transfer functions of the system @@ -431,79 +443,91 @@ We identify the various transfer functions of the system
G = identifyPlant();
save('./active_damping/mat/plants.mat', 'G', '-append');
+The sensitivity to disturbances are shown on figure 1. +
+ + + - ++All the files (data and Matlab scripts) are accessible here. +
+ +Integral Force Feedback is applied. -In section 2.1, IFF is applied on a uni-axial system to understand its behavior. +In section 2.1, IFF is applied on a uni-axial system to understand its behavior. Then, it is applied on the simscape model.
-
Figure 3: Integral Force Feedback applied to a 1dof system
+Figure 4: Integral Force Feedback applied to a 1dof system
@@ -563,8 +587,8 @@ This is attainable if we have:
Let define the system parameters. @@ -618,17 +642,17 @@ And the closed loop system is computed below.
Let's load the undamped plant: @@ -639,14 +663,14 @@ Let's load the undamped plant:
-Let's look at the transfer function from actuator forces in the nano-hexapod to the force sensor in the nano-hexapod legs for all 6 pairs of actuator/sensor (figure 5). +Let's look at the transfer function from actuator forces in the nano-hexapod to the force sensor in the nano-hexapod legs for all 6 pairs of actuator/sensor (figure 6).
--
Figure 5: Transfer function from forces applied in the legs to force sensor (png, pdf)
+Figure 6: Transfer function from forces applied in the legs to force sensor (png, pdf)
@@ -658,26 +682,27 @@ The controller for each pair of actuator/sensor is:
-The corresponding loop gains are shown in figure 6. +The corresponding loop gains are shown in figure 7.
-Let's initialize the system prior to identification.
initializeGround(); +initializeInputs(); +initializeGround(); initializeGranite(); initializeTy(); initializeRy(); @@ -714,7 +739,20 @@ We identify the system dynamics now that the IFF controller is ON.
-As shown on figure 7: +And we save the damped plant for further analysis +
+save('./active_damping/mat/plants.mat', 'G_iff', '-append'); ++
+As shown on figure 8:
-
Figure 7: Sensitivity to disturbance once the IFF controller is applied to the system (png, pdf)
+Figure 8: Sensitivity to disturbance once the IFF controller is applied to the system (png, pdf)
minreal
.
Now, look at the new damped plant to control.
-It damps the plant (resonance of the nano hexapod as well as other resonances) as shown in figure 9. +It damps the plant (resonance of the nano hexapod as well as other resonances) as shown in figure 10.
--However, it increases coupling at low frequency (figure 10). +However, it increases coupling at low frequency (figure 11).
-save('./active_damping/mat/plants.mat', 'G_iff', '-append'); --
@@ -805,31 +833,38 @@ Integral Force Feedback:
+All the files (data and Matlab scripts) are accessible here. +
+ +In the Relative Motion Control (RMC), a derivative feedback is applied between the measured actuator displacement to the actuator force input.
-
Figure 11: Relative Motion Control applied to a 1dof system
+Figure 12: Relative Motion Control applied to a 1dof system
@@ -882,8 +917,8 @@ This corresponds to a gain:
Let define the system parameters. @@ -937,17 +972,17 @@ And the closed loop system is computed below.
Let's load the undamped plant: @@ -958,14 +993,14 @@ Let's load the undamped plant:
-Let's look at the transfer function from actuator forces in the nano-hexapod to the measured displacement of the actuator for all 6 pairs of actuator/sensor (figure 13). +Let's look at the transfer function from actuator forces in the nano-hexapod to the measured displacement of the actuator for all 6 pairs of actuator/sensor (figure 14).
--
Figure 13: Transfer function from forces applied in the legs to leg displacement sensor (png, pdf)
+Figure 14: Transfer function from forces applied in the legs to leg displacement sensor (png, pdf)
@@ -978,26 +1013,27 @@ A Low pass Filter is added to make the controller transfer function proper.
-The obtained loop gains are shown in figure 14. +The obtained loop gains are shown in figure 15.
-Let's initialize the system prior to identification.
initializeGround(); +initializeInputs(); +initializeGround(); initializeGranite(); initializeTy(); initializeRy(); @@ -1034,40 +1070,8 @@ We identify the system dynamics now that the RMC controller is ON.
-As shown in figure 15, RMC control succeed in lowering the sensitivity to disturbances near resonance of the system. +And we save the damped plant for further analysis.
- - - - - - -save('./active_damping/mat/plants.mat', 'G_rmc', '-append');@@ -1075,8 +1079,43 @@ As shown in figure 15, RMC control succeed in lowering
@@ -1091,31 +1130,38 @@ Relative Motion Control:
+All the files (data and Matlab scripts) are accessible here. +
+ +In the Relative Motion Control (RMC), a feedback is applied between the measured velocity of the platform to the actuator force input.
-
Figure 18: Direct Velocity Feedback applied to a 1dof system
+Figure 19: Direct Velocity Feedback applied to a 1dof system
@@ -1131,7 +1177,7 @@ In terms of the stage deformation \(d = x - w\): (ms^2 + cs + k) d = -ms^2 w + F_d + F \end{equation}
-The direct velocity feedback law shown in figure 18 is: +The direct velocity feedback law shown in figure 19 is:
\begin{equation} K = -g @@ -1168,8 +1214,8 @@ This corresponds to a gain:Let's load the undamped plant: @@ -1268,69 +1314,231 @@ Let's load the undamped plant:
-Let's look at the transfer function from actuator forces in the nano-hexapod to the measured velocity of the nano-hexapod platform in the direction of the corresponding actuator for all 6 pairs of actuator/sensor (figure 20). -
- --The plant looks to complicated to be reasonably controlled. +Let's look at the transfer function from actuator forces in the nano-hexapod to the measured velocity of the nano-hexapod platform in the direction of the corresponding actuator for all 6 pairs of actuator/sensor (figure 21).
-Figure 21: Transfer function from forces applied in the legs to leg velocity sensor (png, pdf)
-Direct Velocity Feedback: +The controller is defined below and the obtained loop gain is shown in figure 22.
-+Let's initialize the system prior to identification. +
+initializeInputs(); +initializeGround(); +initializeGranite(); +initializeTy(); +initializeRy(); +initializeRz(); +initializeMicroHexapod(); +initializeAxisc(); +initializeMirror(); +initializeNanoHexapod(struct('actuator', 'piezo')); +initializeSample(struct('mass', 50)); ++
+And initialize the controllers. +
+K = tf(zeros(6)); +save('./mat/controllers.mat', 'K', '-append'); +K_iff = tf(zeros(6)); +save('./mat/controllers.mat', 'K_iff', '-append'); +K_rmc = tf(zeros(6)); +save('./mat/controllers.mat', 'K_rmc', '-append'); +K_dvf = -K_dvf*eye(6); +save('./mat/controllers.mat', 'K_dvf', '-append'); ++
+We identify the system dynamics now that the RMC controller is ON. +
+G_dvf = identifyPlant();
+
++And we save the damped plant for further analysis. +
+save('./active_damping/mat/plants.mat', 'G_dvf', '-append');
+Direct Velocity Feedback: +
+ +load('./active_damping/mat/plants.mat', 'G', 'G_iff', 'G_rmc', 'G_dvf'); ++
-A schematic of the uniaxial model used for simulations is represented in figure 1. +A schematic of the uniaxial model used for simulations is represented in figure 1.
@@ -384,7 +384,7 @@ The control signal \(u\) is: -
Figure 1: Schematic of the uniaxial model used
@@ -393,11 +393,11 @@ The control signal \(u\) is:Few active damping techniques will be compared in order to decide which sensor is to be included in the system. -Schematics of the active damping techniques are displayed in figure 2. +Schematics of the active damping techniques are displayed in figure 2.
-
Figure 2: Comparison of used active damping techniques
@@ -405,16 +405,16 @@ Schematics of the active damping techniques are displayed in figure -Let's start by study the undamped system.
We initialize all the stages with the default parameters. @@ -426,8 +426,8 @@ All the controllers are set to 0 (Open Loop).
We identify the dynamics of the system. @@ -490,19 +490,19 @@ Finally, we save the identified system dynamics for further analysis.
We show several plots representing the sensitivity to disturbances:
Figure 3: Sensitivity to disturbances (png, pdf)
@@ -510,7 +510,7 @@ We show several plots representing the sensitivity to disturbances: --The transfer function from the force \(F\) applied by the nano-hexapod to the position of the sample \(D\) is shown in figure 5. +The transfer function from the force \(F\) applied by the nano-hexapod to the position of the sample \(D\) is shown in figure 5. It corresponds to the plant to control.
-
Figure 6: Uniaxial IFF Control Schematic
load('./uniaxial/mat/plants.mat', 'G'); @@ -562,7 +562,7 @@ Let's look at the transfer function from actuator forces in the nano-hexapod to -+-
Figure 7: Transfer function from forces applied in the legs to force sensor (png, pdf)
@@ -577,7 +577,7 @@ The controller for each pair of actuator/sensor is:+--3.2 Identification
++3.2 Identification
-Let's initialize the system prior to identification. @@ -669,18 +669,18 @@ G_iff.OutputName = {
-3.3 Sensitivity to Disturbance
++3.3 Sensitivity to Disturbance
-+ -+-
Figure 10: Sensitivity to force disturbances in various stages when IFF is applied (png, pdf)
@@ -688,11 +688,11 @@ G_iff.OutputName = {-3.4 Damped Plant
++3.4 Damped Plant
-+ --3.5 Conclusion
++3.5 Conclusion
-@@ -713,25 +713,25 @@ Integral Force Feedback:
-4 Relative Motion Control
++4 Relative Motion Control
In the Relative Motion Control (RMC), a derivative feedback is applied between the measured actuator displacement to the actuator force input.
-+-
Figure 12: Uniaxial RMC Control Schematic
-4.1 Control Design
++4.1 Control Design
load('./uniaxial/mat/plants.mat', 'G'); @@ -743,7 +743,7 @@ Let's look at the transfer function from actuator forces in the nano-hexapod to -+-
Figure 13: Transfer function from forces applied in the legs to leg displacement sensor (png, pdf)
@@ -759,7 +759,7 @@ A Low pass Filter is added to make the controller transfer function proper.+--4.2 Identification
++4.2 Identification
Let's initialize the system prior to identification. @@ -852,18 +852,18 @@ G_rmc.OutputName = { -
-4.3 Sensitivity to Disturbance
++4.3 Sensitivity to Disturbance
-+ -+-
Figure 16: Sensitivity to force disturbances in various stages when RMC is applied (png, pdf)
@@ -871,11 +871,11 @@ G_rmc.OutputName = {-4.4 Damped Plant
++4.4 Damped Plant
-+ --4.5 Conclusion
++4.5 Conclusion
-@@ -896,25 +896,25 @@ Relative Motion Control:
-5 Direct Velocity Feedback
++5 Direct Velocity Feedback
In the Relative Motion Control (RMC), a feedback is applied between the measured velocity of the platform to the actuator force input.
-+-
Figure 18: Uniaxial DVF Control Schematic
-5.1 Control Design
++5.1 Control Design
-load('./uniaxial/mat/plants.mat', 'G'); @@ -922,7 +922,7 @@ In the Relative Motion Control (RMC), a feedback is applied between the measured+-
Figure 19: Transfer function from forces applied in the legs to leg velocity sensor (png, pdf)
@@ -934,7 +934,7 @@ In the Relative Motion Control (RMC), a feedback is applied between the measured+--5.2 Identification
++5.2 Identification
-Let's initialize the system prior to identification. @@ -1026,18 +1026,18 @@ G_dvf.OutputName = {
-5.3 Sensitivity to Disturbance
++5.3 Sensitivity to Disturbance
-+ -+-
Figure 22: Sensitivity to force disturbances in various stages when DVF is applied (png, pdf)
@@ -1045,11 +1045,11 @@ G_dvf.OutputName = {-5.4 Damped Plant
++5.4 Damped Plant
-+ --5.5 Conclusion
++-5.5 Conclusion
@@ -1069,15 +1069,15 @@ Direct Velocity Feedback:
-6 Comparison of Active Damping Techniques
++6 Comparison of Active Damping Techniques
--6.1 Load the plants
++6.1 Load the plants
-load('./uniaxial/mat/plants.mat', 'G', 'G_iff', 'G_rmc', 'G_dvf'); @@ -1086,11 +1086,11 @@ Direct Velocity Feedback:-6.2 Sensitivity to Disturbance
++6.2 Sensitivity to Disturbance
-+
Figure 24: Sensitivity to Ground Motion - Comparison (png, pdf)
@@ -1098,21 +1098,21 @@ Direct Velocity Feedback: -+ -+ -+--6.3 Damped Plant
++6.3 Damped Plant
- --6.4 Conclusion
++6.4 Conclusion
-+
Table 1: Comparison of proposed active damping techniques @@ -1205,7 +1205,7 @@ The next step is to take into account the power spectral density of each disturb diff --git a/uniaxial/index.org b/uniaxial/index.org index ca57bb0..6c1ece8 100644 --- a/uniaxial/index.org +++ b/uniaxial/index.org @@ -2217,7 +2217,7 @@ Direct Velocity Feedback: title('$F_{rz}$ to $D$'); hold on; plot(freqs, abs(squeeze(freqresp(G ('D', 'Frz'), freqs, 'Hz'))), 'k-' , 'DisplayName', 'OL'); - plot(freqs, abs(squeeze(freqresp(G_iff('D', 'Frz'), freqs, 'Hz'))), 'k' , 'DisplayName', 'IFF'); + plot(freqs, abs(squeeze(freqresp(G_iff('D', 'Frz'), freqs, 'Hz'))), 'k:' , 'DisplayName', 'IFF'); plot(freqs, abs(squeeze(freqresp(G_rmc('D', 'Frz'), freqs, 'Hz'))), 'k--', 'DisplayName', 'RMC'); plot(freqs, abs(squeeze(freqresp(G_dvf('D', 'Frz'), freqs, 'Hz'))), 'k-.', 'DisplayName', 'DVF'); hold off; -