diff --git a/aPID.py b/aPID.py
new file mode 100644
index 0000000..e29b75b
--- /dev/null
+++ b/aPID.py
@@ -0,0 +1,76 @@
+import numpy as np
+
+class AdaptivePIDNP:
+    """ PID class implemented for numpy integration. 
+
+    ...
+
+    Attributes
+    ----------
+    Kp : float64
+        Proportional gain.
+    Ki : float64
+        Integral gain.
+    Kd : float64
+        Derivative gain.
+    rbf_network : RBFNetwork object
+        RBF network class instance.
+
+
+    Methods
+    -------
+    update(target, measured_value, dt):
+        Updates the control signal.    
+    """
+    def __init__(self, Kp, Ki, Kd, rbf_network):
+        """ Constructs PID gains and RBF network.
+
+        Parameters
+        ----------
+            Kp : float64
+                Proportional gain.
+            Ki : float64
+                Integral gain.
+            Kd : float64
+                Derivative gain.
+            rbf_network : RBFNetwork object
+                RBF network class instance.
+        """
+        self.Kp = Kp
+        self.Ki = Ki
+        self.Kd = Kd
+        self.rbf_network = rbf_network
+        self.prev_err = 0
+        self.error = 0
+        self.integral = 0
+        self.derivative = 0
+
+    def update(self, target, measured_value, dt):
+        """ Update the control signal according to error and adapt with RBF
+        network predictions. 
+
+        Parameters
+        ----------
+            target : float64
+                Target setpoint.
+            measured_value : float64
+                Actual value.
+            dt : float64
+                Timestep.
+
+        Returns
+        -------
+        Control signal.
+        """
+        self.error = target - measured_value
+        self.integral += self.error * dt
+        self.derivative = (self.error - self.prev_err) / dt
+
+        u = (self.Kp * self.error) + (self.Ki * self.integral) + (self.Kd*self.derivative)
+
+        gain_adapt = self.rbf_network.predict(np.array([self.error, self.integral, self.derivative]))
+        u += gain_adapt
+
+        self.prev_err = self.error
+        return u
+        
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