jensandClaude Sonnet 4.6
95eacabe28
temp_controller_smith.py still relied on Kalman filtering and Pot's old get_temperature_intermediate(), both removed by the recent Pot/Kalman simplification. Rebuild it using two Pot model copies (zero-delay and delayed) and the same backward-difference heat rate as temp_controller.py, combined into the classic Smith correction: theta_ist = theta_model_fast + (theta_plant - theta_model_delay). Also fix set_model_power() never being called, so the internal model actually receives the controller's power output, and fill in demo_temp_controller_smith.py to exercise it end-to-end with an extra plot of plant vs. fast-model vs. delayed-model temperature. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
62 lines
2.1 KiB
Python
Executable File
62 lines
2.1 KiB
Python
Executable File
from components.plant.pot import Pot
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from components.pid.temp_controller_base import TempControllerBase
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from components.pid.tc_constants import States
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class TempController(TempControllerBase):
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def __init__(self, dt, params, model_params):
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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})
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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)
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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 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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self.heatrate_ist = 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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if self.heatrate_soll_set > 0:
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self.pid_hold.scale(1.0/self.heatrate_soll_set)
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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)
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