DSO-ControlLab

DSO-ControlLab is a small, reproducible research bench for testing the core DSO idea in control systems:

Move as many decisions as possible before runtime, then execute a verified deterministic plan.

For the broader project direction, see VISION.md and ROADMAP.md.

The current prototype compares:

• PID: tuned online by deterministic random search.
• LQR: fixed state-feedback controller for linear second-order worlds.
• MPC: short-horizon online planner.
• DSO: offline-selected fixed controller plan with explicit CPU, memory and jitter contract.

It generates random second-order worlds, runs every controller on each world, measures control quality and runtime uncertainty, verifies DSO plans against deployment contracts, then prints aggregate statistics.

Quick Start

python3 -m dso_controllab --worlds 1000 --seed 7

For a fast smoke run:

python3 -m dso_controllab --worlds 50 --seed 1

Metrics

• iae: integral absolute error, lower is better.
• overshoot: maximum response above the target.
• energy: mean squared control effort.
• wcet_us: estimated worst-case execution time.
• jitter_us: estimated runtime timing spread.
• contract_pass_rate: fraction of worlds satisfying the DSO contract.
• score: combined quality/resource cost.

DSO Contract

The default deployment contract is intentionally simple:

Resource layer:

• CPU cycles per step: <= 180
• RAM bytes: <= 96
• WCET: <= 8 us
• jitter: <= 0.8 us

Control layer:

• IAE: <= 4.0
• overshoot: <= 0.9
• max absolute output: <= 8.0
• final error: <= 1.25

Runtime layer:

• saturation fraction: <= 45%
• no NaN / Inf

See CONTRACTS.md for the design model.

This makes the difference visible: MPC may improve quality on some worlds, but it performs more runtime search. DSO compiles the selected controller into a smaller fixed plan with bounded memory and timing, and rejects it if verification fails.

Project Shape

src/dso_controllab/
  cli.py          command-line runner
  contracts.py    resource/control/runtime/deployment contracts
  controllers.py  PID/LQR/MPC/DSO controllers
  experiment.py   random worlds, simulation, summary stats
  metrics.py      scoring and statistical helpers
  verifier.py     pre-deployment verification pass
  world.py        plant model
tests/
  test_smoke.py

Next Research Steps

1. Add genetic programming controller synthesis.
2. Add 1000-world CSV export and plots.
3. Add paired statistical tests against PID/LQR/MPC.
4. Add verification traces: bounds, saturation, failure modes.
5. Replace estimated resource costs with measured embedded targets.