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Shunichi09 2020-04-07 20:45:10 +09:00
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Environments.md
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@ -39,7 +39,7 @@ R = diag[0.1, 0.1]
X_g denote the goal states. X_g denote the goal states.
## [CatpoleEnv (Swing up)]((PythonLinearNonlinearControl/envs/cartpole.py)) ## [CatpoleEnv (Swing up)](PythonLinearNonlinearControl/envs/cartpole.py)
System equation. System equation.

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README.md
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# PythonLinearNonLinearControl # PythonLinearNonLinearControl
@ -116,21 +116,21 @@ It should be noted that **Model** and **Environment** are different. As mentione
<img src="assets/concept.png" width="500"> <img src="assets/concept.png" width="500">
## Model ## [Model](PythonLinearNonlinearControl/models/)
System model. For an instance, in the case that a model is linear, this model should have a form, "x[k+1] = Ax[k] + Bu[k]". System model. For an instance, in the case that a model is linear, this model should have a form, "x[k+1] = Ax[k] + Bu[k]".
If you use gradient based control method, you are preferred to implement the gradients of the model, other wise the controllers use numeric gradients. If you use gradient based control method, you are preferred to implement the gradients of the model, other wise the controllers use numeric gradients.
## Planner ## [Planner](PythonLinearNonlinearControl/planners/)
Planner make the goal states. Planner make the goal states.
## Controller ## [Controller](PythonLinearNonlinearControl/controllers/)
Controller calculate the optimal inputs by using the model by using the algorithms. Controller calculate the optimal inputs by using the model by using the algorithms.
## Runner ## [Runner](PythonLinearNonlinearControl/runners/)
Runner runs the simulation. Runner runs the simulation.