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