forked from M-Labs/thermostat
linuswck
9acff86547
- 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
287 lines
9.5 KiB
Python
287 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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self._peak_count = 0
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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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