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https://github.com/WallabyLester/RBF-aPID-Controller.git
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75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
import numpy as np
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class AdaptivePIDNP:
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""" PID class implemented for numpy integration.
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...
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Attributes
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----------
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Kp : float64
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Proportional gain.
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Ki : float64
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Integral gain.
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Kd : float64
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Derivative gain.
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rbf_network : RBFNetwork object
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RBF network class instance.
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Methods
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-------
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update(target, measured_value, dt):
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Updates the control signal.
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"""
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def __init__(self, Kp, Ki, Kd, rbf_network):
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""" Constructs PID gains and RBF network.
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Parameters
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----------
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Kp : float64
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Proportional gain.
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Ki : float64
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Integral gain.
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Kd : float64
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Derivative gain.
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rbf_network : RBFNetwork object
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RBF network class instance.
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"""
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self.Kp = Kp
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self.Ki = Ki
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self.Kd = Kd
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self.rbf_network = rbf_network
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self.prev_err = 0
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self.error = 0
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self.integral = 0
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self.derivative = 0
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def update(self, target, measured_value, dt):
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""" Update the control signal according to error and adapt with RBF
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network predictions.
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Parameters
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----------
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target : float64
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Target setpoint.
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measured_value : float64
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Actual value.
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dt : float64
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Timestep.
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Returns
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-------
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Control signal.
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"""
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self.error = target - measured_value
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self.integral += self.error * dt
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self.derivative = (self.error - self.prev_err) / dt
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u = (self.Kp * self.error) + (self.Ki * self.integral) + (self.Kd*self.derivative)
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gain_adapt = self.rbf_network.predict(np.array([self.error, self.integral, self.derivative]))
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u += gain_adapt
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self.prev_err = self.error
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return u
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