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add PID autotune code

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Co-Authored-By: topquark12 <aw@m-labs.hk>
Co-Committed-By: topquark12 <aw@m-labs.hk>
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topquark12 2021-01-06 11:02:52 +08:00 committed by sb10q
parent e94601f54f
commit 73dd6d9154
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import math
import logging
from collections import deque, namedtuple
from enum import Enum
from pytec.client import Client
# Based on hirshmann pid-autotune libiary
# See https://github.com/hirschmann/pid-autotune
# Which is in turn based on a fork of Arduino PID AutoTune Library
# See https://github.com/t0mpr1c3/Arduino-PID-AutoTune-Library
class PIDAutotuneState(Enum):
STATE_OFF = 'off'
STATE_RELAY_STEP_UP = 'relay step up'
STATE_RELAY_STEP_DOWN = 'relay step down'
STATE_SUCCEEDED = 'succeeded'
STATE_FAILED = 'failed'
class PIDAutotune:
PIDParams = namedtuple('PIDParams', ['Kp', 'Ki', 'Kd'])
PEAK_AMPLITUDE_TOLERANCE = 0.05
_tuning_rules = {
"ziegler-nichols": [0.6, 1.2, 0.075],
"tyreus-luyben": [0.4545, 0.2066, 0.07214],
"ciancone-marlin": [0.303, 0.1364, 0.0481],
"pessen-integral": [0.7, 1.75, 0.105],
"some-overshoot": [0.333, 0.667, 0.111],
"no-overshoot": [0.2, 0.4, 0.0667]
}
def __init__(self, setpoint, out_step=10, lookback=60,
noiseband=0.5, sampletime=1.2):
if setpoint is None:
raise ValueError('setpoint must be specified')
self._inputs = deque(maxlen=round(lookback / sampletime))
self._setpoint = setpoint
self._outputstep = out_step
self._noiseband = noiseband
self._out_min = -out_step
self._out_max = out_step
self._state = PIDAutotuneState.STATE_OFF
self._peak_timestamps = deque(maxlen=5)
self._peaks = deque(maxlen=5)
self._output = 0
self._last_run_timestamp = 0
self._peak_type = 0
self._peak_count = 0
self._initial_output = 0
self._induced_amplitude = 0
self._Ku = 0
self._Pu = 0
def state(self):
"""Get the current state."""
return self._state
def output(self):
"""Get the last output value."""
return self._output
def tuning_rules(self):
"""Get a list of all available tuning rules."""
return self._tuning_rules.keys()
def get_pid_parameters(self, tuning_rule='ziegler-nichols'):
"""Get PID parameters.
Args:
tuning_rule (str): Sets the rule which should be used to calculate
the parameters.
"""
divisors = self._tuning_rules[tuning_rule]
kp = self._Ku * divisors[0]
ki = divisors[1] * self._Ku / self._Pu
kd = divisors[2] * self._Ku * self._Pu
return PIDAutotune.PIDParams(kp, ki, kd)
def run(self, input_val, time_input):
"""To autotune a system, this method must be called periodically.
Args:
input_val (float): The temperature input value.
time_input (float): Current time in seconds.
Returns:
`true` if tuning is finished, otherwise `false`.
"""
now = time_input * 1000
if (self._state == PIDAutotuneState.STATE_OFF
or self._state == PIDAutotuneState.STATE_SUCCEEDED
or self._state == PIDAutotuneState.STATE_FAILED):
self._state = PIDAutotuneState.STATE_RELAY_STEP_UP
self._last_run_timestamp = now
# check input and change relay state if necessary
if (self._state == PIDAutotuneState.STATE_RELAY_STEP_UP
and input_val > self._setpoint + self._noiseband):
self._state = PIDAutotuneState.STATE_RELAY_STEP_DOWN
logging.debug('switched state: {0}'.format(self._state))
logging.debug('input: {0}'.format(input_val))
elif (self._state == PIDAutotuneState.STATE_RELAY_STEP_DOWN
and input_val < self._setpoint - self._noiseband):
self._state = PIDAutotuneState.STATE_RELAY_STEP_UP
logging.debug('switched state: {0}'.format(self._state))
logging.debug('input: {0}'.format(input_val))
# set output
if (self._state == PIDAutotuneState.STATE_RELAY_STEP_UP):
self._output = self._initial_output + self._outputstep
elif self._state == PIDAutotuneState.STATE_RELAY_STEP_DOWN:
self._output = self._initial_output - self._outputstep
# respect output limits
self._output = min(self._output, self._out_max)
self._output = max(self._output, self._out_min)
# identify peaks
is_max = True
is_min = True
for val in self._inputs:
is_max = is_max and (input_val >= val)
is_min = is_min and (input_val <= val)
self._inputs.append(input_val)
# we don't trust the maxes or mins until the input array is full
if len(self._inputs) < self._inputs.maxlen:
return False
# increment peak count and record peak time for maxima and minima
inflection = False
# peak types:
# -1: minimum
# +1: maximum
if is_max:
if self._peak_type == -1:
inflection = True
self._peak_type = 1
elif is_min:
if self._peak_type == 1:
inflection = True
self._peak_type = -1
# update peak times and values
if inflection:
self._peak_count += 1
self._peaks.append(input_val)
self._peak_timestamps.append(now)
logging.debug('found peak: {0}'.format(input_val))
logging.debug('peak count: {0}'.format(self._peak_count))
# check for convergence of induced oscillation
# convergence of amplitude assessed on last 4 peaks (1.5 cycles)
self._induced_amplitude = 0
if inflection and (self._peak_count > 4):
abs_max = self._peaks[-2]
abs_min = self._peaks[-2]
for i in range(0, len(self._peaks) - 2):
self._induced_amplitude += abs(self._peaks[i]
- self._peaks[i+1])
abs_max = max(self._peaks[i], abs_max)
abs_min = min(self._peaks[i], abs_min)
self._induced_amplitude /= 6.0
# check convergence criterion for amplitude of induced oscillation
amplitude_dev = ((0.5 * (abs_max - abs_min)
- self._induced_amplitude)
/ self._induced_amplitude)
logging.debug('amplitude: {0}'.format(self._induced_amplitude))
logging.debug('amplitude deviation: {0}'.format(amplitude_dev))
if amplitude_dev < PIDAutotune.PEAK_AMPLITUDE_TOLERANCE:
self._state = PIDAutotuneState.STATE_SUCCEEDED
# if the autotune has not already converged
# terminate after 10 cycles
if self._peak_count >= 20:
self._output = 0
self._state = PIDAutotuneState.STATE_FAILED
return True
if self._state == PIDAutotuneState.STATE_SUCCEEDED:
self._output = 0
logging.debug('peak finding successful')
# calculate ultimate gain
self._Ku = 4.0 * self._outputstep / \
(self._induced_amplitude * math.pi)
print('Ku: {0}'.format(self._Ku))
# calculate ultimate period in seconds
period1 = self._peak_timestamps[3] - self._peak_timestamps[1]
period2 = self._peak_timestamps[4] - self._peak_timestamps[2]
self._Pu = 0.5 * (period1 + period2) / 1000.0
print('Pu: {0}'.format(self._Pu))
for rule in self._tuning_rules:
params = self.get_pid_parameters(rule)
print('rule: {0}'.format(rule))
print('Kp: {0}'.format(params.Kp))
print('Ki: {0}'.format(params.Ki))
print('Kd: {0}'.format(params.Kd))
return True
return False
def main():
# Auto tune parameters
# Thermostat channel
channel = 0
# Target temperature of the autotune routine, celcius
target_temperature = 30
# Value by which output will be increased/decreased from zero, amps
output_step = 1
# Reference period for local minima/maxima, seconds
lookback = 3
# Determines by how much the input value must
# overshoot/undershoot the setpoint, celcius
noiseband = 1.5
# logging.basicConfig(level=logging.DEBUG)
tec = Client()
data = next(tec.report_mode())
ch = data[channel]
tuner = PIDAutotune(target_temperature, output_step,
lookback, noiseband, ch['interval'])
for data in tec.report_mode():
try:
ch = data[channel]
# Workaround for report_mode may yeild empty object
except KeyError:
continue
temperature = ch['temperature']
if (tuner.run(temperature, ch['time'])):
break
tuner_out = tuner.output()
tec.set_param("pwm", channel, "i_set", tuner_out)
tec.set_param("pwm", channel, "i_set", 0)
if __name__ == "__main__":
main()