jensandClaude Sonnet 4.6
b3923fb1c0
Two root causes for the visible power dip at every ramp-to-ramp step boundary: 1. One-tick cascade delay: heatrate_soll was computed from pid_hold.get_y() before pid_hold.process() ran in that tick, so the first tick of a new ramp used the stale hold-phase output (≈0) as the rate setpoint, driving pid_heat to near zero. Fix: move heatrate_soll computation inside process_pid(), after pid_hold.process(). 2. Cold-start reset: pid_heat.reset() at HOLD→HEAT discarded the hold-phase integral, so pid_heat had to ramp up from zero on every new step. Fix: remove the reset (bumpless transfer) — the hold-phase integral is a warm starting point that carries the baseline hold power into the new ramp. Together these eliminate the ~1s dip to near-zero and replace the slow 20–30s integral ramp-up with an immediate start at roughly hold-level power, climbing monotonically to the target. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
71 lines
2.6 KiB
Python
Executable File
71 lines
2.6 KiB
Python
Executable File
from components.plant.pot import Pot
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from components.pid.temp_controller_base import TempControllerBase, States
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class TempController(TempControllerBase):
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def __init__(self, dt):
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TempControllerBase.__init__(self, dt)
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# last_theta_ist / theta_ist_filtered / beta / dt all live in the base -
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# see TempControllerBase.__init__ and _compute_heatrate().
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# Fast model: same plant model but with zero transport delay, used
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# to predict the current temperature without the dead time.
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self.model = Pot(dt)
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# Delayed model: keeps the plant's assumed transport delay, so it
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# can be compared like-for-like against the real (delayed) measurement.
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self.model_delay = Pot(dt)
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self.theta_ist_plant = 0
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self.theta_ist_model = 0
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self.theta_ist_model_delay = 0
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def set_model_plant_params(self, model_params):
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self.model.set_plant_params({**model_params, 'Td': 0})
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self.model_delay.set_plant_params(model_params)
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def set_model_power(self, power):
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self.model.set_power(power)
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self.model_delay.set_power(power)
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def set_ambient_temperature(self, theta_amb):
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self.model.set_ambient_temperature(theta_amb)
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self.model_delay.set_ambient_temperature(theta_amb)
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def is_configured(self):
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return super().is_configured() and self.model.is_configured() and self.model_delay.is_configured()
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def on_state_entered(self, state):
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if state in (States.HEAT, States.COOL):
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self.model.initial(self.theta_ist)
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self.model_delay.initial(self.theta_ist)
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def post_pid(self):
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self.model.process()
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self.model_delay.process()
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def process(self):
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self._require_params()
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if not (self.model.is_configured() and self.model_delay.is_configured()):
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raise RuntimeError(
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"{}.process(): model plant params and/or ambient temperature not set - "
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"call set_model_plant_params() and set_ambient_temperature() first".format(type(self).__name__))
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self.theta_ist_plant = self.theta_ist_set
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self.theta_ist_model = self.model.get_temperature()
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self.theta_ist_model_delay = self.model_delay.get_temperature()
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# Smith predictor: use the fast model's prediction, corrected by the
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# mismatch between the real (delayed) plant and the delayed model,
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# so the dead time drops out of the feedback path.
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self.theta_ist = self.theta_ist_model + (self.theta_ist_plant - self.theta_ist_model_delay)
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self._compute_heatrate(self.theta_ist)
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# Compensate for max heat rate to reduce overshoot
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hold_scale = 1.0/self.heatrate_soll_set if self.heatrate_soll_set > 0 else 1.0
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theta_err = self.theta_soll_set - self.theta_ist
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diff = self.theta_soll_set - self.theta_ist
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self.process_fsm(diff)
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self.process_pid(theta_err, hold_scale)
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