SITCOMTN-092
M1M3 Force Balance System - Inertia Compensation#
Abstract
The M1M3 Force Balance System was engineered to mitigate the influence of gravity/elevation forces, thermal fluctuations, inertial forces.
This technical note presents an initial analysis of the Force Balance System’s performance when implementing corrections to account for inertial effects.
Note
This technote is a work-in-progress.
Introduction#
What is the Inertia Compensation System?
Balance Forces
Acceleration Forces
Velocity Forces
Booster Valves
What are the RRGG codes we see around? Why are they important?
-
Why do we have different motion settings?
Single Slew Analysis#
During the full-speed sky mapping mission, the telescope will move very quickly, generating significant inertial forces. To counteract these forces, the Inertial Compensation System (ICS) is employed. The ICS consists of accelerometers and gyroscopic sensors that measure the telescope’s motion and activate during slews in elevation, azimuth, or both axes. This system is critical for minimizing the physical effects of inertial forces on the telescope’s structure. The test involves short slews of 3.5 degrees (long slews defined as > 3.5 degrees and short slews as ≤ 3.5 degrees). The objective is to measure forces in the six HardPoints (HPs), ensuring they remain below the breakaway limits. The nominal breakaway limit, previously measured is 3000 N, defines the maximum force a hardpoint can withstand before disengaging to protect the mirror. Breakawy testing involved applying compression and tension forces to each hardpoint until breakaway occurred, confirming that the limits were within the expected range.
Dynamic Test and Hardpoint Forces#
The dynamic test measures the force on the six HPs, and the measured forces should ideally remain at zero during slews. The HPs should not hold excessive forces, in other words, forces measured should not overstep the 15% of the breakway limits (450 N). This is important because overloaded forces can damage the mirror in the long-term.
Test Configurations#
The tests were conducted on two observation days: 2024-01-03 and 2024-01-05.
100% Velocity, Acceleration, and Jerk (2024-01-03):
Forces on the Hardpoints stay within the nominal breakaway limit (3000 N) but exceed the 30% fatigue limit (900 N).
This configuration poses a risk of mirror damage due to stress and fatigue.
Figure 1. ICS performance when performing a 3.5 deg slew at 100% velocity, acceleration, and jerk. The top y-axes show the measured hardpoint forces. The middle and bottom y-axes show the torques and velocities for Azimuth in Elevation over the same time window. Since the measured forces are trespassing the fatigue limit, running the survey in this configuration can cause damage to the mirror.
40% Velocity, Acceleration, and Jerk (2024-01-05):
Forces on the Hardpoints remained within the 15% operational limit (450 N).
This configuration is safe for long-term operation, minimizing stress on the mirror.
Figure 2. ICS performance when performing a 3.5 deg slew at 40% velocity, acceleration, and jerk. For this configuration, the measured forces are within the operational limits.
The ICS effectively compensated for inertial forces at moderate speeds (40% velocity, acceleration, and jerk). However, at TMA maximum motion settings (100%), the measured forces exceeded the fatigue limits, raising concerns about long-term mirror safety. To ensure the telescope’s safety and longevity, slews should be configured to maintain Hardpoint forces within the 15% operational limit (450 N).
Histogram Analysis#
The same data collected on the observation days, 2024-01-03 and 2024-01-05, is displayed as a histogram to organize the data into groups for these specific observation days. The histogram illustrates the slews performed at 100% and 40% velocity, along with the corresponding acceleration and jerk, which reached certain minimum and maximum values during the slewing process.
Test Configurations#
100% Velocity, Acceleration, and Jerk (2024-01-03):
It is observed that almost every slew performed in this 100% configuration exceeds the negative and positive fatigue limits of 900 N, which poses a significant danger to the mirror in the long term due to stress and fatigue.
Figure 3. Histogram showing the count of slews performed at 100% velocity, acceleration, and jerk that reached a certain minimum and maximum values during a slew.
40% Velocity, Acceleration, and Jerk (2024-01-05):
It is observed that almost every slew obtained during soak tests remains within the fatigue limits of 900 N, with the majority close to or within the operational limits of 450 N.
Figure 4. Histogram showing the count of slews performed at 40% velocity, acceleration, and jerk that reached a certain minimum and maximum values during a slew.
Key Limits:
Nominal Breakaway Limit: 3000 N (absolute maximum force hardpoints can handle).
Fatigue Limit: 30% of 3000 N (900 N); forces above this risk long-term mirror damage.
Operational Limit: 15% of 3000 N (450 N); target for safe, long-term operation.
M1M3 and M2 Surrogates Test Campaigns#
When did they happen? Dates.
What tests did we execute? Those were BLOCK tickets, instead of test cases. List them.
Final results - add a link to SPIE Paper. There is no need to repeat information.
ComCam on Sky Campaign#
What were the settings used?
What are the results?
Add links to the existing notebooks, plots, etc.
Add interpretation to these results.
M1M3 actuator movies#
Craig Lage - 20-Apr-23 The 17 tons of mirror are supported by 156 pneumatic actuators where 44 are single-axis and provide support only on the axial direction, 100 are dual-axis providing support in the axial and lateral direction, and 12 are dual-axis providing support in the axial and cross lateral directions. Positioning is provided by 6 hard points in a hexapod configuration which moves the mirror to a fixed operational position that shall be maintained during telescope operations. The remaining optical elements will be moved relative to this position in order to align the telescope optics. Support and optical figure correction is provided by 112 dual axis and 44 single axis pneumatic actuators.
Ticket SITCOM-763#
M1M3 should be raised for this test. (Check this tbc)
We tested the M1M3 force balance system by applying external force over the surrogate. The force was applied by stepping on random position on the surrogate surface.
Date: 18.04.23 15.30 - 15.40h CLT.
We looked at the M1M3 EUI at the measured forces at the Actuator 2D map.
The expected result was that we see smooth gradients depending on where people were stepping.
We see the movement in the applied actuator forces. See attached video.
For detailed offline analysis: Create an animation of the 2D map for this period with a fixed color scale. Use the nominal position as a reference position.
Prepare the notebook#
python # Directory to store the data
% dir_name = "/home/c/cslage/u/MTM1M3/movies/"
%
% # Times to make the plot
% start = "2023-04-18 16:10:00Z"
% end = "2023-04-18 16:15:00Z"
%
% autoScale = True
% # The following are only used if autoScale = False
% zmin = 0.0 # In nt
% zmax = 2000.0 # In nt
% lateral_max = 1500.0 # In nt
%
% # The following average the first 100 data points
% # and subtract these from the measurements
% # If subtractBasline = False, the unmodified values will be plotted
% subtract_baseline = True
% baseline_t0 = 0.0
% baseline_t1 = 100.0
%
% # The following allows you to plot only every nth data point
% # If this value is 1, a frame will be made for every data point
% # Of course, this takes longer
% # If this value is 50, it will make a frame every second
% frame_n = 50
python import asyncio import glob import os import shlex import subprocess import sys from pathlib import Path from datetime import datetime import matplotlib.pyplot as plt
import numpy as np
from astropy.time import Time, TimeDelta
from matplotlib.colors import LightSource
from lsst.ts.xml.tables.m1m3 import FAOrientation, FATable, FAType
from lsst_efd_client import EfdClient
Set up the necessary subroutines#
def actuator_layout(ax):
""" Plot a visualization of the actuator locations and types Parameters ---------- ax : a matplotlib.axes object
Returns
-------
No return, only the ax object which was input
"""
ax.set_xlabel("X position (m)")
ax.set_ylabel("Y position (m)")
ax.set_title("M1M3 Actuator positions and type\nHardpoints are approximate", fontsize=18)
types = [
[FAType.SAA, FAOrientation.NA, 'o', 'Z', 'b'],
[FAType.DAA, FAOrientation.Y_PLUS, '^', '+Y','g'],
[FAType.DAA, FAOrientation.Y_MINUS, 'v', '-Y', 'cyan'],
[FAType.DAA, FAOrientation.X_PLUS, '>', '+X', 'r'],
[FAType.DAA, FAOrientation.X_MINUS, '<', '-X', 'r'],
]
for [type, orient, marker, label, color] in types:
xs = []
ys = []
for fa in FATable:
x = fa.x_position
y = fa.y_position
if fa.actuator_type == type and \
fa.orientation == orient:
xs.append(x)
ys.append(y)
else:
continue
ax.scatter(xs, ys, marker=marker, color=color, s=200, label=label)
# Now plot approximate hardpoint location
Rhp = 3.1 # Radius in meters
for i in range(6):
theta = 2.0 * np.pi / 6.0 * float(i)
if i == 0:
ax.scatter(Rhp * np.cos(theta), Rhp * np.sin(theta), marker='o', color='magenta', \
s=200, label='HP')
else:
ax.scatter(Rhp * np.cos(theta), Rhp * np.sin(theta), marker='o', color='magenta', \
s=200, label='_nolegend_')
ax.legend(loc='lower left', fontsize=9)
return
def bar_chart_z(df, df_zero, ax, index, zmin, zmax):
""" Plot a 3D bar chart of the actuator Z forces
Parameters
----------
df: pandas dataframe
The pandas dataframe object with the force actuator data
df_zero: pandas dataframe
The pandas dataframe object of the quiescent force data
which will be subtracted off from the force actuator data
ax : a matplotlib.axes object
index: 'int'
The index of the movie frame
zmin: 'float'
The minimum force value for the plot
zmax: 'float'
The maximum force value for the plot
Returns
-------
No return, only the ax object which was input
"""
ax.set_xlabel("X position (m)")
ax.set_ylabel("Y position (m)")
ax.set_zlabel("Force (nt)")
ax.set_title("M1M3 Actuator Z forces", fontsize=18)
light_source = LightSource(azdeg=180, altdeg=78)
grey_color = '0.9'
colors = []
xs = []
ys = []
for fa in FATable:
x = fa.x_position
y = fa.y_position
xs.append(x)
ys.append(y)
if fa.actuator_type == FAType.SAA:
colors.append('blue'); colors.append('blue')
colors.append(grey_color); colors.append(grey_color)
colors.append(grey_color); colors.append(grey_color)
else:
if fa.orientation in [FAOrientation.Y_PLUS, FAOrientation.Y_MINUS]:
colors.append('green'); colors.append('green')
colors.append(grey_color); colors.append(grey_color)
colors.append(grey_color); colors.append(grey_color)
else:
colors.append('red'); colors.append('red')
colors.append(grey_color); colors.append(grey_color)
colors.append(grey_color); colors.append(grey_color)
zs = np.zeros([len(FATable)])
for fa in FATable:
name=f"zForce{fa.index}"
zs[fa.index] = df.iloc[index][name] - df_zero.iloc[0][name]
dxs = 0.2 * np.ones([len(FATable)])
dys = 0.2 * np.ones([len(FATable)])
bottom = np.zeros([len(FATable)])
ax.bar3d(xs, ys, bottom, dxs, dys, zs, shade=True, alpha=0.5, \
lightsource=light_source, color=colors)
ax.set_zlim(zmin, zmax)
ax.view_init(elev=30., azim=225)
return
def heat_map_z(df, df_zero, ax, index, zmin, zmax):
""" Plot a "heat map" of the actuator Z forces
Parameters
----------
df: pandas dataframe
The pandas dataframe object with the force actuator data
df_zero: pandas dataframe
The pandas dataframe object of the quiescent force data
which will be subtracted off from the force actuator data
ax : a matplotlib.axes object
index: 'int'
The index of the movie frame
zmin: 'float'
The minimum force value for the plot
zmax: 'float'
The maximum force value for the plot
Returns
-------
No return, only the ax object which was input
"""
ax.set_xlabel("X position (m)")
ax.set_ylabel("Y position (m)")
ax.set_title("M1M3 Actuator Z forces (nt)", fontsize=18)
types = [
[FAType.SAA, FAOrientation.NA, 'o', 'Z'],
[FAType.DAA, FAOrientation.Y_PLUS, '^', '+Y'],
[FAType.DAA, FAOrientation.Y_MINUS, 'v', '-Y'],
[FAType.DAA, FAOrientation.X_PLUS, '>', '+X'],
[FAType.DAA, FAOrientation.X_MINUS, '<', '-X'],
]
for [type, orient, marker, label] in types:
xs = []
ys = []
zs = []
for fa in FATable:
x = fa.x_position
y = fa.y_position
if fa.actuator_type == type and \
fa.orientation == orient:
xs.append(x)
ys.append(y)
name=f"zForce{fa.index}"
zs.append(df.iloc[index][name] - df_zero.iloc[0][name])
im = ax.scatter(xs, ys, marker=marker, c=zs, cmap='RdBu_r', \
vmin=zmin, vmax=zmax, s=50, label=label)
plt.colorbar(im, ax=ax,fraction=0.055, pad=0.02, cmap='RdBu_r')
return
def lateral_forces(df, df_zero, ax, index, force_max):
""" Plot a 2D whisker plot of the actuator X and Y forces
Parameters
----------
df: pandas dataframe
The pandas dataframe object with the force actuator data
df_zero: pandas dataframe
The pandas dataframe object of the quiescent force data
which will be subtracted off from the force actuator data
ax : a matplotlib.axes object
index: 'int'
The index of the movie frame
force_max: 'float'
maximum force values for scaling the whisker arrows
Returns
-------
No return, only the ax object which was input
"""
ax.set_xlabel("X position (m)")
ax.set_ylabel("Y position (m)")
ax.set_title("M1M3 lateral forces (nt)", fontsize=18)
ax.set_xlim(-4.5,4.5)
ax.set_ylim(-4.5,4.5)
types = [
[FAType.DAA, FAOrientation.Y_PLUS, '^', '+Y','g'],
[FAType.DAA, FAOrientation.Y_MINUS, 'v', '-Y', 'cyan'],
[FAType.DAA, FAOrientation.X_PLUS, '>', '+X', 'r'],
[FAType.DAA, FAOrientation.X_MINUS, '<', '-X', 'r'],
]
for [type, orient, marker, label, color] in types:
xs = []
ys = []
arrow_xs = []
arrow_ys = []
for fa in FATable:
x = fa.x_position
y = fa.y_position
if fa.actuator_type == type and \
fa.orientation == orient:
xs.append(x)
ys.append(y)
if orient == FAOrientation.X_PLUS:
name = f"xForce{fa.x_index}"
arrow_xs.append(df.iloc[index][name] / force_max)
arrow_ys.append(0.0)
elif orient == FAOrientation.X_MINUS:
name = f"xForce{fa.x_index}"
arrow_xs.append(-df.iloc[index][name] / force_max)
arrow_ys.append(0.0)
elif orient == FAOrientation.Y_PLUS:
name = f"yForce{fa.y_index}"
arrow_xs.append(0.0)
arrow_ys.append(df.iloc[index][name] / force_max)
else:
name = f"yForce{fa.y_index}"
arrow_xs.append(0.0)
arrow_ys.append(-df.iloc[index][name] / force_max)
else:
continue
ax.scatter(xs, ys, marker=marker, color=color, s=50, label=label)
for ii in range(len(xs)):
ax.arrow(xs[ii], ys[ii], arrow_xs[ii], arrow_ys[ii], color=color)
ax.plot([-4.0,-3.0], [-4.0,-4.0], color='g')
ax.text(-4.0, -4.3, f"{force_max} nt")
return
def get_zero_values_and_limits(df, subtract_baseline, t0, t1):
""" Plot a 2D whisker plot of the actuator X and Y forces
Parameters
----------
df: pandas dataframe
The pandas dataframe object with the force actuator data.
subtract_baseline : 'bool'
Determines whether or not to subtract off a baseline value from the plots.
t0: 'float'
The time from the beginning of the dataframe when the baseline
quiescent period (which will be subtracted off) begins.
t1: 'float'
The time from the beginning of the dataframe when the baseline
quiescent period (which will be subtracted off) ends.
Returns
-------
zmin: 'float'
The minimum force value for the Z force plots
zmax: 'float'
The maximum force value for the Z force plots
lateral_max: 'float'
The maximum force value for the X,Y whisker plots
df_zero: pandas dataframe
The pandas dataframe object of the quiescent force data
which will be subtracted off from the force actuator data
"""
df_zero = df.head(1)
for column_name in df_zero.columns:
try:
if subtract_baseline:
df_zero.iloc[0, df_zero.columns.get_loc(column_name)] = \
np.median(df[column_name].values[t0:t1])
else:
df_zero.iloc[0, df_zero.columns.get_loc(column_name)] = 0.0
except:
continue
# Now calculate the limits
zmin = 0.0; ymin = 0.0; xmin = 0.0; zmax = 0.0; ymax = 0.0; xmax = 0.0
for fa in FATable:
name = f"zForce{fa.z_index}"
zmin = min(zmin, np.min(df[name] - df_zero.iloc[0][name]))
zmax = max(zmax, np.max(df[name] - df_zero.iloc[0][name]))
if fa.y_index is not None:
name = f"yForce{fa.y_index}"
ymin = min(ymin, np.min(df[name] - df_zero.iloc[0][name]))
ymax = max(ymax, np.max(df[name] - df_zero.iloc[0][name]))
if fa.x_index is not None:
name = f"xForce{fa.x_index}"
xmin = min(xmin, np.min(df[name] - df_zero.iloc[0][name]))
xmax = max(xmax, np.max(df[name] - df_zero.iloc[0][name]))
lateral_max = max(xmax, ymax, -xmin, -ymin)
return [round(zmin), round(zmax), round(lateral_max), df_zero]
Now generate the frames#
This will take some time
client = EfdClient('usdf_efd')
forces = await client.select_time_series("lsst.sal.MTM1M3.forceActuatorData", "*",
Time(start, scale='utc'), Time(end, scale='utc'))
timestamp = forces.index[0].isoformat().split('.')[0].replace('-','').replace(':','')
os.makedirs(Path(dir_name) / f"movie_{timestamp}", exist_ok=True)
[auto_zmin, auto_zmax, auto_lateral_max, forces_zero] = \
get_zero_values_and_limits(forces, subtract_baseline, baseline_t0, baseline_t1)
if autoScale:
zmin = auto_zmin
zmax = auto_zmax
lateral_max = auto_lateral_max
# Build the individual frames
fig = plt.figure(figsize=(16,16))
for n in range(0, len(forces), frame_n):
ax1 = fig.add_subplot(2,2,1)
actuator_layout(ax1)
ax2 = fig.add_subplot(2,2,2, projection='3d')
bar_chart_z(forces, forces_zero, ax2, n, zmin, zmax)
ax3 = fig.add_subplot(2,2,3)
lateral_forces(forces, forces_zero, ax3, n, lateral_max)
ax4 = fig.add_subplot(2,2,4)
heat_map_z(forces, forces_zero, ax4, n, zmin, zmax)
plt.savefig(f"{dir_name}/movie_{timestamp}/Frame_{n:05d}.png")
plt.clf()
len(forces)
Now build the movie#
print(f"\033[1mThe movie name will be: {dir_name}movie_{timestamp}/m1m3_movie.mp4\033[0m")
command = f"ffmpeg -pattern_type glob -i '{dir_name}movie_{timestamp}/*.png' -f mp4 -vcodec libx264 -pix_fmt yuv420p -framerate 50 -y {dir_name}movie_{timestamp}/m1m3_movie.mp4" args = shlex.split(command) build_movie = subprocess.Popen(args) build_movie.wait()
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