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 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("output", channel, "i_set", tuner_out) tec.set_param("output", channel, "i_set", 0) if __name__ == "__main__": main()