Added tensorflow example to first order simulation.

first_order_sim.py

@@ -1,8 +1,11 @@
 import matplotlib.pyplot as plt
 import numpy as np
+import tensorflow as tf
 
 from RBF_numpy import RBFNetwork
-from aPID import AdaptivePIDNP
+from aPID_numpy import AdaptivePIDNP
+from RBF_tf import RBFAdaptiveModel
+from aPID_tf import AdaptivePIDTf
 
 def simulate_system(controller, target, dt, T):
     """ Simulate control model as first order system.
@@ -36,6 +39,7 @@ def simulate_system(controller, target, dt, T):
     return time, measurements
 
 if __name__ == "__main__":
+    # numpy implementation
     np.random.seed(20)
 
     rbf_np = RBFNetwork(input_dim=3, n_centers=5)
@@ -50,8 +54,27 @@ if __name__ == "__main__":
     plt.axhline(y=target, color="r", linestyle="--", label="Target")
     plt.xlabel("Time (s)")
     plt.ylabel("Output")
-    plt.title("Adaptive RBF Neural PID Controller")
+    plt.title("Adaptive RBF Neural PID Controller Numpy")
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+    # tensorflow implementation
+    rbf_tf = RBFAdaptiveModel(n_centers=5, input_dim=3)
+    rbf_tf.compile(optimizer="adam", loss="mean_squared_error")
+
+    apid_tf = AdaptivePIDTf(Kp=7.0, Ki=0.5, Kd=0.01, rbf_model=rbf_tf)
+
+    target = 1.0
+    dt = 0.1
+    T = 10.0
+    time, measurements = simulate_system(apid_tf, target, dt, T)
+
+    plt.plot(time, measurements, label="Measured Value")
+    plt.axhline(y=target, color="r", linestyle="--", label="Target")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Output")
+    plt.title("Adaptive RBF Neural PID Controller TF")
     plt.legend()
     plt.grid()
     plt.show()
-