Эксперимент: H-Infinity Disturbance-Attenuating Latent Observer (#1139)

{ "worked": true, "confidence": 8, "verdict": "Built a scalar H-infinity latent observer MVP with numerical storage-metric search, bounded-real LMI evaluation, exact disturbance-transfer verification, and time-domain dissipation checks. The feasible observer gain K=1.7804 had LMI maximum eigenvalue -0.00098, exact H-infinity gain 0.8166 below gamma=0.9, and zero-disturbance V decay rate 4.36 versus the required 0.4. The unconstrained high-gain baseline K=10 converged faster but was not LMI-feasible and exceeded the target gain with exact H-infinity gain 0.9615, demonstrating the claimed stability/attenuation tradeoff on this toy system.", "metrics": { "baseline": "K=10.0; LMI max eigenvalue=0.07708 (infeasible); exact H-infinity gain=0.96154 > gamma=0.9; observed V decay rate=20.81", "idea": "K=1.78043, P=0.50642; LMI max eigenvalue=-0.000981 (feasible); exact H-infinity gain=0.81655 < gamma=0.9; observed V decay rate=4.362 > 2*alpha=0.4; random-input energy ratio=0.01979" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 observer_experiment.py", "files": [ "observer_experiment.py", "results.json" ], "limitations": "Only a scalar linear continuous-time error system was tested; no neural predictor, learned gain, GRU comparison, Lorenz-63 system, partial-observation training, parameter/time-varying P, or large-scale computational overhead was evaluated. The gain and storage metric were selected by grid search rather than jointly learned." }