Pith Number

pith:M72SKI4K

pith:2026:M72SKI4KBZODWWRAKMCJGZMLA3

not attested not anchored not stored refs resolved

Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control

Syed Pouladi

A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.

arxiv:2605.16754 v1 · 2026-05-16 · eess.SY · cs.SY

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{M72SKI4KBZODWWRAKMCJGZMLA3}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Theoretical analysis establishes ISS of the latent dynamics and a finite ultimate bound on tracking error. Numerical experiments demonstrate a 31% reduction in tracking RMSE, a 44% improvement in control smoothness, and near-zero latent stability violation rate across environment-switching scenarios.

C2weakest assumption

That a contraction-constrained residual neural network can capture unmodeled nonlinear effects while still admitting an explicit input-to-state stability certificate without loss of expressiveness or violation of the fiber-bundle geometry.

C3one line summary

SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.

References

18 extracted · 18 resolved · 1 Pith anchors

[1] Hamiltonian systems and transformation in Hilbert space, 1931

[2] Linear predictors for nonlinear dynamical sys- tems: Koopman operator meets model predictive control 2018

[3] On input-to- state stability verification of identified models obtained by Koopman operator, 2025

[4] ICODE: Modeling dynam- ical systems with extrinsic input information, 2025

[5] Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures, 2025

Receipt and verification

First computed	2026-05-20T00:03:19.898866Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9

Aliases

arxiv: 2605.16754 · arxiv_version: 2605.16754v1 · doi: 10.48550/arxiv.2605.16754 · pith_short_12: M72SKI4KBZOD · pith_short_16: M72SKI4KBZODWWRA · pith_short_8: M72SKI4K

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/M72SKI4KBZODWWRAKMCJGZMLA3 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "dd223013e3cc28839aa67c5cb51204ec68717bc848995fdb63b5a67a5be8e36e",
    "cross_cats_sorted": [
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-16T02:09:16Z",
    "title_canon_sha256": "195c5934030c775484a50e46d80685468ef451d58f311de60fd78558065a5a54"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16754",
    "kind": "arxiv",
    "version": 1
  }
}