jensandClaude Sonnet 4.6
abcd011a9f
Covers Smith predictor parameter identification (C, M, L, Td) from log data, model-plant replay verification, and controller performance metrics. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NSo8R6GjoBQdStB3j67zSs
120 lines
3.7 KiB
Markdown
120 lines
3.7 KiB
Markdown
# Temperature controller calibration
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## Controller overview
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`components/pid/temp_controller_smith.py` implements a Smith predictor. Each tick it computes:
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```
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theta_ist = theta_ist_model + (theta_ist_plant − theta_ist_model_delay)
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```
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`theta_ist_model` comes from a fast internal `Pot` (transport delay Td = 0);
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`theta_ist_model_delay` from a second `Pot` with the full configured Td.
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The correction term `(theta_ist_plant − theta_ist_model_delay)` removes the dead
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time from the feedback path.
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The plant model (`components/plant/pot.py`) integrates:
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```
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temp += (delayed_power_in − L·M·(temp − theta_amb)) / (M·C) · dt
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```
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Four parameters must be calibrated:
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| Parameter | Unit | Meaning |
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|---|---|---|
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| C | J/(kg·K) | Specific thermal capacity |
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| M | kg | Mass of pot + water + grain |
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| L | W/(kg·K) | Heat loss coefficient |
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| Td | s | Transport (heater-to-sensor) delay |
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## 1. Verify model-plant match: replay simulation
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Feed the **recorded `power_eff`** from a log file into a fresh `Pot` instance
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with the same parameters and compare the simulated temperature to `temp_ist`.
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```python
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from components.plant.pot import Pot
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pot = Pot(dt=1.0)
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pot.set_plant_params({'C': C, 'M': M, 'L': L, 'Td': Td})
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pot.set_ambient_temperature(theta_amb)
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pot.initial(samples[0]['temp_ist'])
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sim_temp = []
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for s in samples:
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pot.set_power(s['power_eff'])
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pot.process()
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sim_temp.append(pot.get_temperature())
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```
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Plot `sim_temp` (model) against `temp_ist` (real) and the residual
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`temp_ist − sim_temp`. What divergence tells you:
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| Symptom | Likely cause |
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|---|---|
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| Ramp slopes differ | C·M wrong |
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| Phase shift between power step and temperature rise | Td wrong |
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| Wrong equilibrium temperature during HOLD | L wrong |
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| Residual grows over a long run | Model drift / L temperature-dependent |
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## 2. Parameter identification from log data
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### Td — transport delay
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Cross-correlate `power_eff` with `temp_ist`. The lag at peak correlation is the
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actual Td. Alternatively, find a sharp power step (start of a ramp) and measure
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the visible delay before `temp_ist` begins rising.
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### L — heat loss coefficient
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At steady-state HOLD, `d(temp)/dt ≈ 0`, so all input power compensates losses:
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```
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P_hold = L · M · (T − theta_amb)
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→ L = P_hold / (M · (T − theta_amb))
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```
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Repeat across hold phases at different temperatures and fit a line through
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`(T − theta_amb)` vs `P_hold` for a more robust estimate.
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### C — specific heat capacity
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During a ramp where heat loss is small relative to input power:
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```
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C ≈ power_eff / (M · rate_ist_K_per_s)
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```
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`rate_ist` in the log is K/min; divide by 60 to get K/s. Use a mid-ramp window
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where `power_eff` and `rate_ist` are both stable.
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## 3. Controller performance metrics
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These can be read directly from the log without re-simulation:
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| Metric | How to compute |
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| Overshoot | `max(temp_ist) − temp_soll` after each setpoint step |
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| Settling time | First time `|temp_ist − temp_soll| < threshold` is sustained after a step |
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| Steady-state error | Mean of `temp_soll − temp_ist` during HOLD phase |
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| Rate tracking error | `rate_ist − rate_soll` during RAMP |
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### Smith predictor correction signal
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The term `theta_ist_plant − theta_ist_model_delay` is the Smith correction; a
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growing correction signal indicates model drift. This is not currently written to
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the log. To observe it, log `theta_ist_model` and `theta_ist_model_delay` from
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`temp_controller_smith.py` alongside the existing fields.
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## 4. Suggested addition to analyze_log.py
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A replay panel added to `utils/analyze_log.py` would show:
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- `temp_ist` (blue) vs `sim_temp` (red dashed) — model accuracy at a glance
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- Residual `temp_ist − sim_temp` in a separate subplot — reveals systematic
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parameter errors vs noise floor
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