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forked from M-Labs/thermostat
thermostat/pytec/autotune.py
linuswck 9acff86547 Restructure GUI Code, Improve and Fix Bugs
- Bugs fix:
1. Params Tree user input will not get overwritten
    by incoming report thermostat_data_model.
2. PID Autotune Sampling Period is now set according to Thermostat sampling interval
3. PID Autotune won't get stuck in Fail State
4. Various types disconnection related Bugs
5. Number of Samples stored in the plot cannot be set
6. Limit the max settable output current to be 2000mA

- Improvement:
1. Params Tree settings can be changed with external json
2. Use a Tab system to show a single channel of config instead of two
3. Expose PID Autotune lookback params
4. Icon is changed to Artiq logo

- Restructure:
1. Restructure the code to follow Model-View-Delegate Design Pattern
2024-06-06 17:34:15 +08:00

287 lines
9.5 KiB
Python

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'
STATE_READY = 'ready'
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 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
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()
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 get_tec_pid (self):
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
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
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)
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
logging.debug('Pu: {0}'.format(self._Pu))
for rule in self._tuning_rules:
params = self.get_pid_parameters(rule)
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]
tuner = PIDAutotune(target_temperature, output_step,
lookback, noiseband, ch['interval'])
for data in tec.report_mode():
ch = data[channel]
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()