jensandClaude Sonnet 4.6
f1688d3c71
Sud now parses top-level L/Td fields from sude/*.json (defaulting to 0.2/30, matching the old hardcoded server constants), and derive_plant_params() returns them alongside M/C - constant for the whole brew, unlike M/C which vary per step with grain/water mass. All sude/*.json docs gain explicit L/Td fields. Callers (tasks/sud.py, SudForecastEstimator, demo_sud.py) switch from the narrow set_thermal_params(M, C)/set_model_params(M, C) to the full set_plant_params(params)/set_model_plant_params(params), since derive_plant_params() now always returns all four keys; both narrow setters are removed as dead code. Caught along the way: Pot.set_plant_params() unconditionally rebuilt the Delay ring buffer, which was harmless when only ever called once at construction - but now running on every Sud step change, it was wiping in-flight delayed power at each step boundary. Fixed to only rebuild when Td actually changes; verified this restores the exact original forecast result for sud_0010.json. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DkkuG48uHFCGKe6dPSERFk
76 lines
2.7 KiB
Python
Executable File
76 lines
2.7 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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self.dt = dt
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self.last_theta_ist = 20
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self.heatrate_ist = 0
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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 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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heatrate = (self.theta_ist - self.last_theta_ist)/self.dt*60
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alpha = 0.1
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self.heatrate_ist = (1-alpha) * self.heatrate_ist + alpha*heatrate
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self.last_theta_ist = 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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self.heatrate_soll = self.heatrate_soll_set * self.pid_hold.get_y()
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theta_err = self.theta_soll_set - self.theta_ist
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heatrate_err = self.heatrate_soll - self.heatrate_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, heatrate_err, hold_scale)
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