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‎.gitignore

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*.csv
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*.log
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*.pickle
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*.mp4
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# folders
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.vscode/
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.pytest_cache/
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result/
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.cache/
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.eggs/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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.pytest_cache
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cache/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/

‎.travis.yml

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language: python
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python:
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- 3.7
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install:
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- pip install --upgrade pip setuptools wheel
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- pip install coveralls
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script:
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- coverage run --source=PythonLinearNonlinearControl setup.py test
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after_success:
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- coveralls

‎LICENSE

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MIT License
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3-
Copyright (c) 2018 Shunichi Sekiguchi
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Copyright (c) 2020 Shunichi Sekiguchi
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎PythonLinearNonlinearControl/__init__.py

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‎PythonLinearNonlinearControl/common/__init__.py

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‎PythonLinearNonlinearControl/common/utils.py

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import numpy as np
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‎PythonLinearNonlinearControl/configs/__init__.py

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‎PythonLinearNonlinearControl/configs/first_order_lag.py

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import numpy as np
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class FirstOrderLagConfigModule():
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# parameters
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ENV_NAME = "FirstOrderLag-v0"
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TYPE = "Linear"
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TASK_HORIZON = 1000
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PRED_LEN = 10
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STATE_SIZE = 4
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INPUT_SIZE = 2
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DT = 0.05
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# cost parameters
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R = np.eye(INPUT_SIZE)
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Q = np.eye(STATE_SIZE)
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Sf = np.eye(STATE_SIZE)
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# bounds
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INPUT_LOWER_BOUND = np.array([-0.5, -0.5])
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INPUT_UPPER_BOUND = np.array([0.5, 0.5])
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# DT_INPUT_LOWER_BOUND = np.array([-0.5 * DT, -0.5 * DT])
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# DT_INPUT_UPPER_BOUND = np.array([0.25 * DT, 0.25 * DT])
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DT_INPUT_LOWER_BOUND = None
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DT_INPUT_UPPER_BOUND = None
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def __init__(self):
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"""
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"""
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# opt configs
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self.opt_config = {
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"Random": {
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"popsize": 5000
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},
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"CEM": {
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"popsize": 500,
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"num_elites": 50,
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"max_iters": 15,
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"alpha": 0.3,
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"init_var":1.,
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"threshold":0.001
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},
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"MPPI":{
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"beta" : 0.6,
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"popsize": 5000,
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"kappa": 0.9,
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"noise_sigma": 0.5,
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},
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"iLQR":{
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},
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"cgmres-NMPC":{
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},
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"newton-NMPC":{
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},
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}
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@staticmethod
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def input_cost_fn(u):
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""" input cost functions
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Args:
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u (numpy.ndarray): input, shape(input_size, )
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or shape(pop_size, input_size)
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Returns:
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cost (numpy.ndarray): cost of input, none or shape(pop_size, )
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"""
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return (u**2) * np.diag(FirstOrderLagConfigModule.R)
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@staticmethod
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def state_cost_fn(x, g_x):
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""" state cost function
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Args:
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x (numpy.ndarray): state, shape(pred_len, state_size)
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or shape(pop_size, pred_len, state_size)
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g_x (numpy.ndarray): goal state, shape(state_size, )
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or shape(pop_size, state_size)
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Returns:
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cost (numpy.ndarray): cost of state, none or shape(pop_size, )
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"""
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return ((x - g_x)**2) * np.diag(FirstOrderLagConfigModule.Q)
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@staticmethod
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def terminal_state_cost_fn(terminal_x, terminal_g_x):
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"""
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Args:
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terminal_x (numpy.ndarray): terminal state,
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shape(state_size, ) or shape(pop_size, state_size)
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terminal_g_x (numpy.ndarray): terminal goal state,
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shape(state_size, ) or shape(pop_size, state_size)
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Returns:
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cost (numpy.ndarray): cost of state, none or shape(pop_size, )
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"""
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return ((terminal_x - terminal_g_x)**2) \
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* np.diag(FirstOrderLagConfigModule.Sf)

‎PythonLinearNonlinearControl/configs/make_configs.py

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from .first_order_lag import FirstOrderLagConfigModule
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from .two_wheeled import TwoWheeledConfigModule
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def make_config(args):
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"""
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Returns:
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config (ConfigModule class): configuration for the each env
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"""
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if args.env == "FirstOrderLag":
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return FirstOrderLagConfigModule()
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elif args.env == "TwoWheeledConst" or args.env == "TwoWheeled":
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return TwoWheeledConfigModule()

‎PythonLinearNonlinearControl/configs/two_wheeled.py

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import numpy as np
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class TwoWheeledConfigModule():
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# parameters
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ENV_NAME = "TwoWheeled-v0"
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TYPE = "Nonlinear"
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TASK_HORIZON = 1000
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PRED_LEN = 10
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STATE_SIZE = 3
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INPUT_SIZE = 2
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DT = 0.01
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# cost parameters
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R = np.eye(INPUT_SIZE)
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Q = np.eye(STATE_SIZE)
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Sf = np.eye(STATE_SIZE)
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# bounds
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INPUT_LOWER_BOUND = np.array([-1.5, 3.14])
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INPUT_UPPER_BOUND = np.array([1.5, 3.14])
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def __init__(self):
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"""
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"""
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# opt configs
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self.opt_config = {
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"Random": {
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"popsize": 5000
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},
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"CEM": {
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"popsize": 500,
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"num_elites": 50,
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"max_iters": 15,
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"alpha": 0.3,
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"init_var":1.,
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"threshold":0.001
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},
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"MPPI":{
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"beta" : 0.6,
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"popsize": 5000,
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"kappa": 0.9,
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"noise_sigma": 0.5,
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},
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"iLQR":{
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},
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"NMPC-CGMRES":{
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},
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"NMPC-Newton":{
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},
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}
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@staticmethod
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def input_cost_fn(u):
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""" input cost functions
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Args:
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u (numpy.ndarray): input, shape(input_size, )
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or shape(pop_size, input_size)
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Returns:
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cost (numpy.ndarray): cost of input, none or shape(pop_size, )
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"""
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return (u**2) * np.diag(TwoWheeledConfigModule.R) * 0.1
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@staticmethod
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def state_cost_fn(x, g_x):
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""" state cost function
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Args:
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x (numpy.ndarray): state, shape(pred_len, state_size)
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or shape(pop_size, pred_len, state_size)
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g_x (numpy.ndarray): goal state, shape(state_size, )
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or shape(pop_size, state_size)
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Returns:
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cost (numpy.ndarray): cost of state, none or shape(pop_size, )
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"""
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return ((x - g_x)**2) * np.diag(TwoWheeledConfigModule.Q)
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@staticmethod
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def terminal_state_cost_fn(terminal_x, terminal_g_x):
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"""
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Args:
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terminal_x (numpy.ndarray): terminal state,
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shape(state_size, ) or shape(pop_size, state_size)
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terminal_g_x (numpy.ndarray): terminal goal state,
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shape(state_size, ) or shape(pop_size, state_size)
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Returns:
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cost (numpy.ndarray): cost of state, none or shape(pop_size, )
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"""
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return ((terminal_x - terminal_g_x)**2) \
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* np.diag(TwoWheeledConfigModule.Sf)

‎PythonLinearNonlinearControl/controllers/__init__.py

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