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 # RBF-aPID-Controller
 RBF Neural Net Adaptive PID Controller
+
+Implementations of a radial basis function (RBF) neural network adaptive PID controller. Uses 
+neural net and error information from PID control to adapt the control signal. Provides one 
+adaptation value, using error, integral, and derivative. 
+
+To adapt the PID gains themselves, network outputs must be made to 3 neurons. Example usage 
+with simulated data can be found in `first_order_sim.py`
+
+The method has been implemented in three ways: 
+
+1. TensorFlow : Using TF to build and train the RBF Model.
+
+![TensorFlow](images/tf_impl.png "TensorFlow")
+![TF_Trained](images/trained.png "TF_Trained")
+
+2. Numpy: Using Numpy to build and train the RBF Model.
+
+![Numpy](images/nump_impl.png "Numpy")
+
+3. C++: Written in C++ (requiring `cmath`), for use on embedded systems.