Эксперимент: Proper-Kernel Neural Safety Layer (#1134)

{ "worked": false, "confidence": 9, "verdict": "Built a scalar proper-kernel safety-layer MVP with exact exponential filtering, affine projection, robust residual margin, direct state-margin safeguard, and matched noisy-control evaluation. The frequency-response claim was verified: measured attenuation matched a/sqrt(a^2+omega^2) within 0.42% across tested bandwidths and frequencies. However, the proposed controller did not produce a control-smoothing win: total variation was essentially unchanged for a<=1 and increased for a=3 and a=10, while both methods had zero safety violations.", "metrics": { "baseline": "Memoryless robust CBF: TV=9.16996, violation rate=0.0, infeasibility rate=0.0 for all tested a.", "idea": "Filtered robust CBF: TV=9.16903 at a=0.3, 9.16945 at a=1, 9.19096 at a=3, and 9.48870 at a=10; violation and infeasibility rates were 0.0 for all tested a. Maximum frequency-response relative error was 0.00410." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 proper_kernel_experiment.py", "files": [ "proper_kernel_experiment.py", "results.json" ], "limitations": "Only a scalar xdot=u toy plant and hand-designed noisy policy were tested; no learned MLP/RNN, third-order dynamics, true QP solver, multi-constraint setting, parameter/noise sweep, or GPU experiment was run. The direct h>=delta safeguard is represented by a one-step viability clamp rather than a full slack-variable QP." }