jensandClaude Sonnet 4.6
c6e9258351
Consistent naming: config-sim.json.tpl and config-real.json.tpl. Update README and TODO references accordingly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SH2D4LRuCnhLSzoYerAfJX
72 lines
3.9 KiB
Markdown
72 lines
3.9 KiB
Markdown
# components/pid design backlog
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Findings from a design review, refreshed against the current state of
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`master` after the Kalman-filter removal and Smith-predictor rewrite (see
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git history for `temp_controller.py`/`temp_controller_smith.py`).
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- [x] **FSM thresholds aren't configurable.** Moved into an optional
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`TempCtrl.Thresholds` config section (`HoldIdle`, `HoldHeat`,
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`IdleHeat`, `IdleHold`, `HeatHold`, `HeatIdle`), merged over
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`DEFAULT_THRESHOLDS` (now in `temp_controller_base.py`, formerly
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`tc_constants.py`) so existing configs without the section keep
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working unchanged.
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- [x] **matplotlib imported at module level in production code.**
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`temp_controller.py`, `temp_controller_smith.py`, and `kalman.py`
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used to `from matplotlib.pyplot import ...` just to support their
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`__main__` self-test plots. The plotting demos (and `kalman_eval.py`)
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moved to `scripts/demos/pid/demo_*.py`; production modules no longer
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import matplotlib.
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- [x] **Three Kalman filters share one tuning.** No longer applicable:
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`temp_controller_smith.py` was rewritten to drop Kalman filtering
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entirely (see below), so there's no shared tuning to split anymore.
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- [x] **`Pid.scale()` is a gain-scheduling hack, not anti-windup.**
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Fixed: removed `Pid.scale()`/`self.k` entirely. `Pid.process(err, d,
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scale=1.0)` now takes the gain multiplier as a plain per-call
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argument instead of mutable state, so there's nothing to go stale
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across a `reset()`. `temp_controller*.py` compute `hold_scale =
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1.0/heatrate_soll_set if heatrate_soll_set > 0 else 1.0` themselves
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and pass it through `process_pid(theta_err, heatrate_err,
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hold_scale)` — the overshoot-compensation scheduling logic now lives
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entirely in the temp controller, not the generic `Pid` block.
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- [ ] **No automated tests.** `tests/components/pid/` was added once
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(stdlib `unittest`, no pytest) covering FSM transitions, anti-windup
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clamping, and Kalman convergence, but was removed again before the
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Kalman-filter removal/Smith rewrite, so none of the current
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`temp_controller*.py` behavior (backward-difference + low-pass
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filtered heat rate, the two-model Smith correction) has test
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coverage. This drives a physical heater — worth re-adding.
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- [ ] **`set_model_power` isn't defined on every controller, but
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`brewpi.py` wires it unconditionally.** `brewpi.py` always does
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`heater.set_on_changed("power_set", tc.set_model_power)` regardless
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of `Controller.pid_type`. Only `temp_controller_smith.py` (the Smith
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predictor) defines `set_model_power`; `temp_controller.py` (`"Normal"`)
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does not, so starting the server with `"pid_type": "Normal"` crashes
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at wiring time with `AttributeError: 'TempController' object has no
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attribute 'set_model_power'`. Either add a no-op `set_model_power` to
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`TempControllerBase`, or only wire it when the configured controller
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actually exposes a model to feed.
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- [ ] **Config access is unchecked `dict[key]` everywhere.**
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`params['Hold']`, `params['Td']`, etc., throughout, with no schema
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validation at load time. We already hit this bug class once
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(`config.json.sim`'s `gain`/`Model` nesting mismatch). A small
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schema/dataclass validation layer at config-load would surface
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errors immediately instead of mid-`__init__` — and would have caught
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the dead `TempCtrl.Kalman` / `Model.kn` / `Plant.kn` keys that used
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to sit unused in `config-sim.json.tpl`/`.sim` (since deleted), and the
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`Model.gain`/`Plant.gain` keys that did the same in `config.json.sim`
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before that file was removed entirely — `Pot` dropped `gain`
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entirely, see `components/plant/TODO.md`.
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- [ ] **`kalman.py` is now dead code in production.** Neither
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`temp_controller.py` nor `temp_controller_smith.py` uses `Kalman`
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anymore; the only remaining references are
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`scripts/demos/pid/demo_kalman.py` and `demo_kalman_eval.py`. Decide
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whether to keep it as a documented standalone filtering example or
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delete it along with the demos.
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