{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OJMHRMQY7AFMZFFBMAVP3FGH3S","short_pith_number":"pith:OJMHRMQY","schema_version":"1.0","canonical_sha256":"725878b218f80acc94a1602afd94c7dcbdadb5df960c07d44d0f1eee5315c239","source":{"kind":"arxiv","id":"2410.10387","version":2},"attestation_state":"computed","paper":{"title":"Robust Tracking Control with Neural Network Dynamic Models under Input Perturbations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Changliu Liu, Hanjiang Hu, Huixuan Cheng","submitted_at":"2024-10-14T11:22:39Z","abstract_excerpt":"Robust control problems have significant practical implications since external disturbances can significantly impact the performance of control methods. Existing robust control methods excel at control-affine systems but fail at neural network dynamic models. Developing robust control methods for such systems remains a complex challenge. In this paper, we focus on robust tracking methods for neural network dynamic models. We first propose a reachability analysis tool designed for this system and then introduce how to reformulate a robust tracking problem with reachable sets. In addition, we pr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.10387","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-10-14T11:22:39Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"7f213b4593cbe49ecebbb8a7825f9bf35959bb5206557f8adbee7b2b6f1f6200","abstract_canon_sha256":"9d908eaeacb4815f0e17734fd4785a7a44855dfb359691dc23577dcd37a69858"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:37.002173Z","signature_b64":"ZdUwTUem+Fqpvqq/jVB8UpfogLVDi1tq1+KNSVj4qQku4VSjlGI2PPxwg9RoKDw2YgFbTe5G0O0w955Qd8c0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"725878b218f80acc94a1602afd94c7dcbdadb5df960c07d44d0f1eee5315c239","last_reissued_at":"2026-07-05T11:21:37.001680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:37.001680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Tracking Control with Neural Network Dynamic Models under Input Perturbations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Changliu Liu, Hanjiang Hu, Huixuan Cheng","submitted_at":"2024-10-14T11:22:39Z","abstract_excerpt":"Robust control problems have significant practical implications since external disturbances can significantly impact the performance of control methods. Existing robust control methods excel at control-affine systems but fail at neural network dynamic models. Developing robust control methods for such systems remains a complex challenge. In this paper, we focus on robust tracking methods for neural network dynamic models. We first propose a reachability analysis tool designed for this system and then introduce how to reformulate a robust tracking problem with reachable sets. In addition, we pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10387","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.10387/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.10387","created_at":"2026-07-05T11:21:37.001741+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.10387v2","created_at":"2026-07-05T11:21:37.001741+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10387","created_at":"2026-07-05T11:21:37.001741+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJMHRMQY7AFM","created_at":"2026-07-05T11:21:37.001741+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJMHRMQY7AFMZFFB","created_at":"2026-07-05T11:21:37.001741+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJMHRMQY","created_at":"2026-07-05T11:21:37.001741+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.15643","citing_title":"Safe PDE Boundary Control with Neural Operators","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S","json":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S.json","graph_json":"https://pith.science/api/pith-number/OJMHRMQY7AFMZFFBMAVP3FGH3S/graph.json","events_json":"https://pith.science/api/pith-number/OJMHRMQY7AFMZFFBMAVP3FGH3S/events.json","paper":"https://pith.science/paper/OJMHRMQY"},"agent_actions":{"view_html":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S","download_json":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S.json","view_paper":"https://pith.science/paper/OJMHRMQY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.10387&json=true","fetch_graph":"https://pith.science/api/pith-number/OJMHRMQY7AFMZFFBMAVP3FGH3S/graph.json","fetch_events":"https://pith.science/api/pith-number/OJMHRMQY7AFMZFFBMAVP3FGH3S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S/action/storage_attestation","attest_author":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S/action/author_attestation","sign_citation":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S/action/citation_signature","submit_replication":"https://pith.science/pith/OJMHRMQY7AFMZFFBMAVP3FGH3S/action/replication_record"}},"created_at":"2026-07-05T11:21:37.001741+00:00","updated_at":"2026-07-05T11:21:37.001741+00:00"}