Эксперимент: Koopman-MPC Trust Region for Neural Rollouts (#1230)

{ "worked": true, "confidence": 7, "verdict": "Built a compact Koopman-MPC trust-region toy implementation for a nonlinear damped pendulum, including condensed prediction matrices, spectral-radius capping, bounded corrections, and trust-region projection. The condensed rollout identity was verified to numerical precision (max error 9.9e-10), and capping the fitted model at rho=0.95 reduced 100-step state growth from 1.45x to 0.0083x. In the fixed-seed forecasting test, the MPC version reduced mean 60-step error from 1.983 to 1.457 and predicted-state norm from 2.928 to 0.585, so this toy experiment shows a promising but potentially over-conservative effect.", "metrics": { "baseline": "Mean 60-step rollout error 1.9831; mean predicted norm 2.9284; unconstrained fitted-model 100-step growth 1.4544x.", "idea": "Mean 60-step rollout error 1.4572; mean predicted norm 0.5849; capped-model 100-step growth 0.00830x; condensed-matrix max error 9.93e-10. Trust-radius sweep errors for delta=[0.15,0.3,0.55,1.0,2.0] were [0.7994,0.8028,0.8028,0.8028,0.8028]." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 koopman_mpc_experiment.py", "files": [ "koopman_mpc_experiment.py", "results.json" ], "limitations": "Only a small damped-pendulum toy system was tested, with a linear least-squares model in the observed two-dimensional state rather than a learned lifted latent representation. The controller uses a bounded first-step correction and radial trust-region projection, not a general-purpose inequality-constrained QP solver; no wall-clock speedup, action-conditioned B model, slack variable, spectral-cap sweep, or multi-seed statistical test was evaluated." }