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thermostat/pytec/autotune.py

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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):
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STATE_OFF = "off"
STATE_RELAY_STEP_UP = "relay step up"
STATE_RELAY_STEP_DOWN = "relay step down"
STATE_SUCCEEDED = "succeeded"
STATE_FAILED = "failed"
STATE_READY = "ready"
class PIDAutotune:
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PIDParams = namedtuple("PIDParams", ["Kp", "Ki", "Kd"])
PEAK_AMPLITUDE_TOLERANCE = 0.05
_tuning_rules = {
"ziegler-nichols": [0.6, 1.2, 0.075],
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"tyreus-luyben": [0.4545, 0.2066, 0.07214],
"ciancone-marlin": [0.303, 0.1364, 0.0481],
"pessen-integral": [0.7, 1.75, 0.105],
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"some-overshoot": [0.333, 0.667, 0.111],
"no-overshoot": [0.2, 0.4, 0.0667],
}
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def __init__(
self, setpoint, out_step=10, lookback=60, noiseband=0.5, sampletime=1.2
):
if setpoint is None:
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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
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def setParam(self, target, step, noiseband, sampletime, lookback):
self._setpoint = target
self._outputstep = step
self._out_max = step
self._out_min = -step
self._noiseband = noiseband
self._inputs = deque(maxlen=round(lookback / sampletime))
def setReady(self):
self._state = PIDAutotuneState.STATE_READY
self._peak_count = 0
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def setOff(self):
self._state = PIDAutotuneState.STATE_OFF
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()
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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)
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def get_tec_pid(self):
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divisors = self._tuning_rules["tyreus-luyben"]
kp = self._Ku * divisors[0]
ki = divisors[1] * self._Ku / self._Pu
kd = divisors[2] * self._Ku * self._Pu
return 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
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if (
self._state == PIDAutotuneState.STATE_OFF
or self._state == PIDAutotuneState.STATE_SUCCEEDED
or self._state == PIDAutotuneState.STATE_FAILED
or self._state == PIDAutotuneState.STATE_READY
):
self._state = PIDAutotuneState.STATE_RELAY_STEP_UP
self._last_run_timestamp = now
# check input and change relay state if necessary
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if (
self._state == PIDAutotuneState.STATE_RELAY_STEP_UP
and input_val > self._setpoint + self._noiseband
):
self._state = PIDAutotuneState.STATE_RELAY_STEP_DOWN
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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
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logging.debug("switched state: {0}".format(self._state))
logging.debug("input: {0}".format(input_val))
# set output
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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)
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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):
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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
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amplitude_dev = (
0.5 * (abs_max - abs_min) - self._induced_amplitude
) / self._induced_amplitude
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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
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logging.debug("peak finding successful")
# calculate ultimate gain
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self._Ku = 4.0 * self._outputstep / (self._induced_amplitude * math.pi)
logging.debug("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
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logging.debug("Pu: {0}".format(self._Pu))
for rule in self._tuning_rules:
params = self.get_pid_parameters(rule)
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logging.debug("rule: {0}".format(rule))
logging.debug("Kp: {0}".format(params.Kp))
logging.debug("Ki: {0}".format(params.Ki))
logging.debug("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 = 20
# 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]
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tuner = PIDAutotune(
target_temperature, output_step, lookback, noiseband, ch["interval"]
)
for data in tec.report_mode():
ch = data[channel]
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temperature = ch["temperature"]
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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()