{"paper":{"title":"Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Syed Pouladi","submitted_at":"2026-05-16T02:09:16Z","abstract_excerpt":"Learning-based dynamical models face a persistent tension between expressiveness and formal guarantees: richer model classes improve predictive accuracy, but their stability properties are typically verified only empirically, if at all.\n  This paper proposes \\emph{Stable Fiber-Koopman Residual Dynamics} (SFKD), a unified framework that simultaneously addresses environment-aware geometric consistency, latent-space stability certification, and bounded residual perturbation propagation.\n  Concretely, SFKD constructs a fiber bundle latent manifold whose fibers encode environment-specific dynamics;"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"80717cf60c9cd974d8521c08576d650bbc6fa272db72017e8889914ee360534d"},"source":{"id":"2605.16754","kind":"arxiv","version":1},"verdict":{"id":"6e19c7f2-d663-410e-a38b-5d6c62682ff3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:39:14.534075Z","strongest_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.","one_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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_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.","pith_extraction_headline":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16754/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.802150Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:51:05.095259Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.324190Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.455273Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"767b44b4f13fa0c3618d88e297e80cd187c8a3d85dab40654ec19d88853d87f1"},"references":{"count":18,"sample":[{"doi":"","year":1931,"title":"Hamiltonian systems and transformation in Hilbert space,","work_id":"524f25f5-4df4-4385-8a91-f74139d9984a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Linear predictors for nonlinear dynamical sys- tems: Koopman operator meets model predictive control","work_id":"fd409937-1bb5-4b25-922a-957b1e15792a","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"On input-to- state stability verification of identified models obtained by Koopman operator,","work_id":"e7061d89-e85b-4e83-af15-16ebbaa24466","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"ICODE: Modeling dynam- ical systems with extrinsic input information,","work_id":"6a680ae2-6710-4ead-bdbb-bcb6e65c67b4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures,","work_id":"547ce436-2aec-49cd-ba0b-2955dd6327a0","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":18,"snapshot_sha256":"86d8aa372459075aefbecc020003b848929c47f9a9372666174e8f4c654b77a8","internal_anchors":1},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}