Tennis Racket example, following Windy Pendulum#1
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ecaterina-sur wants to merge 3 commits into
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@ecaterina-sur the code looks correct. my suggestion would be to add the gym environment as a separate python file in the envs folder and then the model can make use of your environment as a callable. would to another round once. thanks |
…tennis racket Stages A–C: train DissipativeSO3HamNODE on single/multi-config free body; FixedInertiaFromState enables exact multi-config generalisation. Stage B adds friction learning (Dw_net). Stage C embeds inertia in state (18D). Stages D–E: IDA-PBC stabilisation u=Kp(ω*−ω); ZOH bound Kp<0.229; saddle Kp_min=0.021; wind SS offset |d|/Kp confirmed to 2%. Stage F: geometric attitude control u=−KR·vee(logm(R*ᵀR))−Kp·eω; almost-global SO(3)×ℝ³ stability; ZOH bound KR<2.00; orientation-velocity duality under wind (|d|/KR for orientation, |d|/Kp for velocity). Add docs/hackmd_advisor.md and docs/hackmd_reference.md. Add venv/ and .DS_Store to .gitignore. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Following the Windy Pendulum Example, I created tennis_racket_3d.py, which simulates the tennis racket motion, integrated with the Heun scheme, and tennis_racket_datagen.py which calls the environment multiple times and stores the data in a pickle. This should allow for various starting configurations to be generated and fed into PHAST.