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Add: Quick start guide
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‎PythonLinearNonlinearControl/configs/__init__.py‎

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from PythonLinearNonlinearControl.configs.cartpole \
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import CartPoleConfigModule # NOQA
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from PythonLinearNonlinearControl.configs.first_order_lag \
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import FirstOrderLagConfigModule # NOQA
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from PythonLinearNonlinearControl.configs.two_wheeled \
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import TwoWheeledConfigModule # NOQA

‎PythonLinearNonlinearControl/controllers/__init__.py‎

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from PythonLinearNonlinearControl.controllers.cem \
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import CEM # NOQA
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from PythonLinearNonlinearControl.controllers.mppi \
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import MPPI # NOQA
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from PythonLinearNonlinearControl.controllers.mppi_williams \
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import MPPIWilliams # NOQA
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from PythonLinearNonlinearControl.controllers.random \
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import RandomShooting # NOQA
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from PythonLinearNonlinearControl.controllers.ilqr \
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import iLQR # NOQA
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from PythonLinearNonlinearControl.controllers.ddp \
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import DDP # NOQA
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from PythonLinearNonlinearControl.controllers.mpc \
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import LinearMPC # NOQA

‎PythonLinearNonlinearControl/envs/__init__.py‎

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from PythonLinearNonlinearControl.envs.cartpole \
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import CartPoleEnv # NOQA
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from PythonLinearNonlinearControl.envs.first_order_lag \
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import FirstOrderLagEnv # NOQA
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from PythonLinearNonlinearControl.envs.two_wheeled \
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import TwoWheeledConstEnv # NOQA
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from PythonLinearNonlinearControl.envs.two_wheeled \
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import TwoWheeledTrackEnv # NOQA

‎PythonLinearNonlinearControl/models/__init__.py‎

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from PythonLinearNonlinearControl.models.cartpole \
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import CartPoleModel # NOQA
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from PythonLinearNonlinearControl.models.first_order_lag \
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import FirstOrderLagModel # NOQA
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from PythonLinearNonlinearControl.models.two_wheeled \
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import TwoWheeledModel # NOQA

‎PythonLinearNonlinearControl/planners/__init__.py‎

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from PythonLinearNonlinearControl.planners.const_planner \
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import ConstantPlanner # NOQA
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from PythonLinearNonlinearControl.planners.closest_point_planner \
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import ClosestPointPlanner # NOQA

‎PythonLinearNonlinearControl/plotters/__init__.py‎

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from PythonLinearNonlinearControl.plotters.animator \
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import Animator
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from PythonLinearNonlinearControl.plotters.plot_func \
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import plot_results

‎PythonLinearNonlinearControl/plotters/animator.py‎

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class Animator():
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""" animation class
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"""
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def __init__(self, args, env):
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def __init__(self, env, args=None):
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"""
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"""
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self.env_name = args.env
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self.result_dir = args.result_dir
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self.controller_type = args.controller_type
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self.env_name = "Env"
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self.result_dir = "./result"
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self.controller_type = "controller"
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if args is not None:
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self.env_name = args.env
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self.result_dir = args.result_dir
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self.controller_type = args.controller_type
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self.interval = env.config["dt"] * 1000. # to ms
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self.plot_func = env.plot_func

‎PythonLinearNonlinearControl/runners/__init__.py‎

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from PythonLinearNonlinearControl.runners.runner \
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import ExpRunner # NOQA

‎README.md‎

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[![GitHub issues open](https://img.shields.io/github/issues/Shunichi09/PythonLinearNonlinearControl.svg)]()
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[![Build Status](https://travis-ci.org/Shunichi09/PythonLinearNonlinearControl.svg?branch=master&service=github)](https://travis-ci.org/Shunichi09/PythonLinearNonlinearControl)
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# PythonLinearNonLinearControl
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# PythonLinearNonlinearControl
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PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
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PythonLinearNonlinearControl is a library implementing the linear and nonlinear control theories in python.
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Due to use only basic libralies (scipy, numpy), this library is easy to extend for your own situations.
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<div><img src="assets/concept.png" width="500"/></div>
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You could know abount our environmets more in [Environments.md](Environments.md)
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# Usage
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# Installation
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## To install this package
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pip install -e .
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```
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## Run Experiments
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You can run the experiments as follows:
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```
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python scripts/simple_run.py --env FirstOrderLag --controller CEM
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```
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**figures and animations are saved in the ./result folder.**
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# Basic concepts
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When we design control systems, we should have **Model**, **Planner**, **Controller** and **Runner** as shown in the figure.
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Please, see more detail in each scripts.
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# Getting started
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## [QuickStart Guide](scripts/quickstart/quickstart.md)
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This is a quickstart guide for users who just want to try PythonLinearNonlinearControl.
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If you have not installed PythonLinearNonLinearControl, please see the section of "how to setup" in README.md
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When we design control systems, we should have Environment, Model, Planner, Controller and Runner.
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Therefore your script contains those Modules.
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First, import each Modules from PythonLinearNonlinearControl.
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```py
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from PythonLinearNonlinearControl import configs
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from PythonLinearNonlinearControl import envs
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from PythonLinearNonlinearControl import models
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from PythonLinearNonlinearControl import planners
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from PythonLinearNonlinearControl import controllers
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from PythonLinearNonlinearControl import runners
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```
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Configs contains each modules configurations such as cost functions, prediction length, ...etc.
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Then you can make each module. (This is example about CEM and CartPole env)
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```py
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config = configs.CartPoleConfigModule()
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env = envs.CartPoleEnv()
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model = models.CartPoleModel(config)
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planner = controllers.CEM(config, model)
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runner = planners.ConstantPlanner(config)
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controller = runners.ExpRunner()
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```
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The preparation for experiment has done!
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Please run the runner.
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```py
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history_x, history_u, history_g = runner.run(env, controller, planner)
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```
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You can get the results of history of state, history of input and history of goal.
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Use that histories to visualize the Animation or Figures.
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(Note FirstOrderEnv does not support animation)
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```py
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# plot results
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plot_results(args, history_x, history_u, history_g=history_g)
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save_plot_data(args, history_x, history_u, history_g=history_g)
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# create animation
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animator = Animator(args, env)
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animator.draw(history_x, history_g)
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```
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## Run Experiments
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You can run the experiments as follows:
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```
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python scripts/simple_run.py --env CartPole --controller CEM --save_anim 1
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```
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**figures and animations are saved in the ./result folder.**
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# Old version
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If you are interested in the old version of this library, that was not a library just examples, please see [v1.0](https://github.com/Shunichi09/PythonLinearNonlinearControl/tree/v1.0)
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title = {PythonLinearNonlinearControl},
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note = "\url{https://github.com/Shunichi09/PythonLinearNonlinearControl}",
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}
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```
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```

‎scripts/quickstart/quickstart.md‎

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# PythonLinearnNonlinearControl Quickstart Guide
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This is a quickstart guide for users who just want to try PythonLinearNonlinearControl.
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If you have not installed PythonLinearNonLinearControl, please see the section of "how to setup" in README.md
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When we design control systems, we should have Environment, Model, Planner, Controller and Runner.
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Therefore your script contains those Modules.
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First, import each Modules from PythonLinearNonlinearControl.
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```py
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from PythonLinearNonlinearControl import configs
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from PythonLinearNonlinearControl import envs
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from PythonLinearNonlinearControl import models
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from PythonLinearNonlinearControl import planners
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from PythonLinearNonlinearControl import controllers
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from PythonLinearNonlinearControl import runners
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```
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Configs contains each modules configurations such as cost functions, prediction length, ...etc.
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Then you can make each module. (This is example about CEM and CartPole env)
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```py
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config = configs.CartPoleConfigModule()
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env = envs.CartPoleEnv()
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model = models.CartPoleModel(config)
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planner = controllers.CEM(config, model)
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runner = planners.ConstantPlanner(config)
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controller = runners.ExpRunner()
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```
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The preparation for experiment has done!
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Please run the runner.
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```py
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history_x, history_u, history_g = runner.run(env, controller, planner)
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```
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You can get the results of history of state, history of input and history of goal.
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Use that histories to visualize the Animation or Figures.
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(Note FirstOrderEnv does not support animation)
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```py
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# plot results
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plot_results(args, history_x, history_u, history_g=history_g)
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save_plot_data(args, history_x, history_u, history_g=history_g)
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# create animation
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animator = Animator(args, env)
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animator.draw(history_x, history_g)
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```

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