jensandClaude Sonnet 4.6
241a796ffa
grain_mass and water_mass now live on each step (defaulted from default.step) instead of being fixed for the whole brew, since both change over a mash (malt going in, water boiling off). Sud.derive_plant_params() takes them as arguments so it can be recomputed per step; the demo re-applies the resulting M/C to both the real plant and the controller's Smith-predictor model on every step change via the new Pot.set_thermal_params()/ TempController.set_model_params(). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CgR9tPaSzFkAwRAUyeaaCD
66 lines
2.3 KiB
Python
Executable File
66 lines
2.3 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, params, model_params, theta_amb=20):
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TempControllerBase.__init__(self, dt, params)
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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, {**model_params, 'Td': 0}, theta_amb)
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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, model_params, theta_amb)
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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_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_model_params(self, M, C):
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self.model.set_thermal_params(M, C)
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self.model_delay.set_thermal_params(M, C)
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def on_state_entered(self, state):
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if state == States.HEAT:
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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.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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