Mirrors_Framework/RBF-aPID-Controller
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mirror of https://github.com/WallabyLester/RBF-aPID-Controller.git synced 2026-05-31 19:46:46 +08:00
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Created PID controller class for tensorflow implementation

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This commit is contained in:
Andru Liu
2024-10-07 19:59:57 -07:00
parent 9e2c25db6c
commit d4820ceada
1 changed files with 22 additions and 22 deletions
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aPID.py → aPID_tf.py
+22 -22
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@@ -1,44 +1,44 @@
import numpy as np import tensorflow as tf
class AdaptivePIDNP: class AdaptivePIDTf:
""" PID class implemented for numpy integration. """ PID class implemented for TensorFlow integration.
... ...
Attributes Attributes
---------- ----------
Kp : float64 Kp : float
Proportional gain. Proportional gain.
Ki : float64 Ki : float
Integral gain. Integral gain.
Kd : float64 Kd : float
Derivative gain. Derivative gain.
rbf_network : RBFNetwork object rbf_model : RBFAdaptiveModel object
RBF network class instance. RBF adaptive model class instance.
Methods Methods
------- -------
update(target, measured_value, dt): update(target, measured_value, dt):
Updates the control signal. Updates the control signal.
""" """
def __init__(self, Kp, Ki, Kd, rbf_network): def __init__(self, Kp, Ki, Kd, rbf_model):
""" Constructs PID gains and RBF network. """ Constructs PID gains, RBF model, and initial PID components.
Parameters Parameters
---------- ----------
Kp : float64 Kp : float
Proportional gain. Proportional gain.
Ki : float64 Ki : float
Integral gain. Integral gain.
Kd : float64 Kd : float
Derivative gain. Derivative gain.
rbf_network : RBFNetwork object rbf_model : RBFAdaptiveModel object
RBF network class instance. RBF adaptive model class instance.
""" """
self.Kp = Kp self.Kp = Kp
self.Ki = Ki self.Ki = Ki
self.Kd = Kd self.Kd = Kd
self.rbf_network = rbf_network self.rbf_model = rbf_model
self.prev_err = 0 self.prev_err = 0
self.error = 0 self.error = 0
self.integral = 0 self.integral = 0
@@ -46,15 +46,15 @@ class AdaptivePIDNP:
def update(self, target, measured_value, dt): def update(self, target, measured_value, dt):
""" Update the control signal according to error and adapt with RBF """ Update the control signal according to error and adapt with RBF
network predictions. model predictions.
Parameters Parameters
---------- ----------
target : float64 target : float
Target setpoint. Target setpoint.
measured_value : float64 measured_value : float
Actual value. Actual value.
dt : float64 dt : float
Timestep. Timestep.
Returns Returns
@@ -67,8 +67,8 @@ class AdaptivePIDNP:
u = (self.Kp * self.error) + (self.Ki * self.integral) + (self.Kd*self.derivative) 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])) control_signal_adapt = self.rbf_model(tf.constant([[self.error, self.integral, self.derivative]])).numpy().flatten()[0]
u += gain_adapt u += control_signal_adapt
self.prev_err = self.error self.prev_err = self.error
return u return u