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1 | 1 | [](LICENSE) |
2 | | -[](https://coveralls.io/github/Shunichi09/PythonLinearNonlinearControl?branch=master) |
3 | | -[](https://travis-ci.org/Shunichi09/PythonLinearNonlinearControl) |
| 2 | +[](https://coveralls.io/github/Shunichi09/PythonLinearNonlinearControl?branch=master&service=github) |
| 3 | +[](https://travis-ci.org/Shunichi09/PythonLinearNonlinearControl) |
4 | 4 |
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5 | 5 | # PythonLinearNonLinearControl |
6 | 6 |
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@@ -116,21 +116,21 @@ It should be noted that **Model** and **Environment** are different. As mentione |
116 | 116 |
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117 | 117 | <img src="assets/concept.png" width="500"> |
118 | 118 |
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119 | | -## Model |
| 119 | +## [Model](PythonLinearNonlinearControl/models/) |
120 | 120 |
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121 | 121 | 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]". |
122 | 122 |
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123 | 123 | If you use gradient based control method, you are preferred to implement the gradients of the model, other wise the controllers use numeric gradients. |
124 | 124 |
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125 | | -## Planner |
| 125 | +## [Planner](PythonLinearNonlinearControl/planners/) |
126 | 126 |
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127 | 127 | Planner make the goal states. |
128 | 128 |
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129 | | -## Controller |
| 129 | +## [Controller](PythonLinearNonlinearControl/controllers/) |
130 | 130 |
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131 | 131 | Controller calculate the optimal inputs by using the model by using the algorithms. |
132 | 132 |
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133 | | -## Runner |
| 133 | +## [Runner](PythonLinearNonlinearControl/runners/) |
134 | 134 |
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135 | 135 | Runner runs the simulation. |
136 | 136 |
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