scenarioMonteCarloAttRW

Overview

Demonstrates how to run basic Monte-Carlo (MC) RW-based attitude simulations. This script duplicates the scenario in scenarioAttitudeFeedbackRW where a 6-DOF spacecraft is orbiting the Earth. Here some simulation parameters are dispersed randomly using a multi threaded Monte-Carlo setup. Reaction Wheel (RW) state effector are added to the rigid spacecraft() hub, and what flight algorithm module is used to control these RWs. The scenario is run in a single configuration: by not using the Jitter model and by using the RW Voltage IO. Given this scenario we can add dispersions to the variables in between each MC run.

The script is found in the folder basilisk/examples and executed by using:

python3 scenarioMonteCarloAttRW.py

For more information on the Attitude Feedback Simulation with RW, please see the documentation on the scenarioAttitudeFeedbackRW file.

Enable Terminal Bar to Show Simulation Progress

To enable progress bar, one need to set showProgressBar data member of class SimulationParameters to true.

monteCarlo = Controller()
monteCarlo.setShowProgressBar(True)

Method setShowProgressBar should be used to set variable showProgressBar as True with the above statement. After enabling the progress bar, all the simulation run by monteCarlo.ExecuteSimulation() and montoCarlo.runInitialConditions will show the progress bar in the terminal.

Setup Changes for Monte-Carlo Runs

In order to set up the multi-threaded MC simulation, the user must first instantiate the Controller class. The function that is being simulated is the set in this class (in this case, it’s defined in the same file as the MC scenario). The user can then set other variables such as the number of runs, the dispersion seeds, and number of cores.

The next important step to setting up the MC runs is to disperse the necessary variables. The dispersions that are set are listed in the following table:

Input	Description of Element	Distribution
Inertial attitude	Using Modified Rodrigues Parameters	Uniform for all 3 rotations between [0, 2 pi]
Inertial rotation rate	Using omega vector	Normal dispersions for each of the rotation components, each of mean 0 and standard deviation 0.25 deg/s
Mass of the hub	Total Mass of the spacecraft	Uniform around +/-5% of expected values. Bounds are [712.5, 787.5]
Center of Mass Offset	Position vector offset on the actual center of mass, and its theoretical position	Normally around a mean [0, 0, 1], with standard deviations of [0.05/3, 0.05/3, 0.1/3]
Inertia Tensor	3x3 inertia tensor. Dispersed by 3 rotations	Normally about mean value of diag(900, 800, 600). Each of the 3 rotations are normally distributed with angles of mean 0 and standard deviation 0.1 deg.
RW axes	The rotation axis for each of the 3 wheels	Normally around a respective means [1,0,0], [0,1,0], and [0,0,1] with respective standard deviations [0.01/3, 0.005/3, 0.005/3], [0.005/3, 0.01/3, 0.005/3], and [0.005/3, 0.005/3, 0.01/3].
RW speeds	The rotation speed for each of the 3 wheels	Uniform around +/-5% of expected values. Bounds are [95, 105], [190, 210], and [285, 315]
Voltage to Torque Gain	The gain between the commanded torque and the actual voltage	Uniform around +/-5% of expected values. Bounds are [0.019, 0.021]

Next a retention policy is used to log the desired data. The simulation can now be run. It returns the failed jobs, which should not occur. When the MC have been executed, the data can be accessed and tested in different ways. This is explained in the example python code comments.

Note

In these Monte Carlo simulations the retained data is stored as the data array with the time information added as the first column. This is the same retained data format as used with BSK 1.x.

Illustration of Simulation Results

saveFigures = False, case = 1, show_plots = True, useDatashader = False

scenarioMonteCarloAttRW.run(saveFigures, case, show_plots)

The scenarios can be run with the followings setups parameters:

Parameters

• saveFigures (bool) – flag if the scenario figures should be saved for html documentation

• case (int) – Case 1 is normal MC, case 2 is initial condition run

• show_plots (bool) – Determines if the script should display plots