Эксперимент: Certified Tube Wrapper for Learned Predictive Control (#819)

{ "worked": true, "confidence": 8, "verdict": "Built a deterministic certified-tube MVP with closed-loop Jacobian propagation, additive plus multiplicative uncertainty conversion, spectral-radius checks, sampled interval validation, and nominal/constant/horizon-dependent control modes with observable fallback logging. The key predictions manifested: the finite-horizon instability boundary was observed at 1.01 versus predicted gamma*Lambda=1.00, scalar steady-state radii matched w/(1-r) within 2e-14 relative error, and multiplicative margin slope matched |B u| exactly. The control mini-experiment showed zero violations for every method and uncertainty level, so it does not establish a practical safety or performance win over the baselines.", "metrics": { "baseline": "Nominal: violation rate 0.0 at beta=0.10, 0.25, 0.40; mean absolute state 0.01809, 0.01804, 0.01801 respectively.", "idea": "Horizon-dependent tube: violation rate 0.0 at all tested beta values; mean absolute state 0.01773, 0.01762, 0.01753; fallback rate 0.0. Math checks: boundary 1.01 observed vs 1.00 predicted, steady-gain max relative error 1.9e-14, multiplicative slope 0.28 observed and predicted, sampled 2D bound ratio 0.862." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 tube_experiment.py", "files": [ "tube_experiment.py", "results.json" ], "limitations": "The plant/controller benchmark is a lightweight scalar surrogate rather than cart-pole or a learned neural dynamics model; no autodiff, QP solver, model training, calibration quantile estimation, action-tube propagation, or stressed infeasibility regime was tested. The controller comparison is therefore only a functional MVP and not evidence of a robust MPC performance win." }