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Python PID
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import time class PID:
"""PID Controller
""" def __init__(self, P=0.2, I=0.0, D=0.0): self.Kp = P
self.Ki = I
self.Kd = D self.sample_time = 0.00
self.current_time = time.time()
self.last_time = self.current_time self.clear() def clear(self):
"""Clears PID computations and coefficients"""
self.SetPoint = 0.0 self.PTerm = 0.0
self.ITerm = 0.0
self.DTerm = 0.0
self.last_error = 0.0 # Windup Guard
self.int_error = 0.0
self.windup_guard = 20.0 self.output = 0.0 def update(self, feedback_value):
"""Calculates PID value for given reference feedback
.. math::
u(t) = K_p e(t) + K_i \int_{0}^{t} e(t)dt + K_d {de}/{dt}
.. figure:: images/pid_1.png
:align: center
Test PID with Kp=1.2, Ki=1, Kd=0.001 (test_pid.py)
"""
error = self.SetPoint - feedback_value self.current_time = time.time()
delta_time = self.current_time - self.last_time
delta_error = error - self.last_error if (delta_time >= self.sample_time):
self.PTerm = self.Kp * error
self.ITerm += error * delta_time if (self.ITerm < -self.windup_guard):
self.ITerm = -self.windup_guard
elif (self.ITerm > self.windup_guard):
self.ITerm = self.windup_guard self.DTerm = 0.0
if delta_time > 0:
self.DTerm = delta_error / delta_time # Remember last time and last error for next calculation
self.last_time = self.current_time
self.last_error = error self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm) def setKp(self, proportional_gain):
"""Determines how aggressively the PID reacts to the current error with setting Proportional Gain"""
self.Kp = proportional_gain def setKi(self, integral_gain):
"""Determines how aggressively the PID reacts to the current error with setting Integral Gain"""
self.Ki = integral_gain def setKd(self, derivative_gain):
"""Determines how aggressively the PID reacts to the current error with setting Derivative Gain"""
self.Kd = derivative_gain def setWindup(self, windup):
"""Integral windup, also known as integrator windup or reset windup,
refers to the situation in a PID feedback controller where
a large change in setpoint occurs (say a positive change)
and the integral terms accumulates a significant error
during the rise (windup), thus overshooting and continuing
to increase as this accumulated error is unwound
(offset by errors in the other direction).
The specific problem is the excess overshooting.
"""
self.windup_guard = windup def setSampleTime(self, sample_time):
"""PID that should be updated at a regular interval.
Based on a pre-determined sampe time, the PID decides if it should compute or return immediately.
"""
self.sample_time = sample_time
