{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AIVWKI3VDHOL2KYIMQYY5USREM","short_pith_number":"pith:AIVWKI3V","schema_version":"1.0","canonical_sha256":"022b65237519dcbd2b0864318ed251232ed8ff5b96452b5d870c26e7c80911e1","source":{"kind":"arxiv","id":"2606.24316","version":1},"attestation_state":"computed","paper":{"title":"Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Frank Allg\\\"ower, Gang Wang, Jian Sun, Wenjie Liu, Yifan Xie, Yuzhou Wei","submitted_at":"2026-06-23T08:54:22Z","abstract_excerpt":"Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing data-driven MPC methods focus on linear systems, and little attention has been given to nonlinear dynamics under disturbances. To fill this gap, we propose a robust data-driven min-max MPC scheme for unknown nonlinear systems with process disturbances. We represent the unknown nonlinear dynamics using vector fields built from a dictionary of basis functions, yielding an equivalent linear form with unknown matrices. The"},"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":"2606.24316","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-06-23T08:54:22Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"a493c92b25268cc9ca58975c4e29cbbb78320d9ac2d11a28d8004a7515ced7b3","abstract_canon_sha256":"74465411953730a1e3358cad62b3d1a64e1ea185247cebbb896d2c9e416fe966"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:49.780265Z","signature_b64":"Sj/RdvgwOr3g0oHuyh1sKPQ+AGucUwF810pLXXQgiTI5NSgPyjVtmzqr89aDzwbHDHnYXkMrKQhBZw/hgdVgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"022b65237519dcbd2b0864318ed251232ed8ff5b96452b5d870c26e7c80911e1","last_reissued_at":"2026-06-24T01:14:49.779844Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:49.779844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-Driven Robust MPC for Unknown Nonlinear Systems via Set-Membership Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Frank Allg\\\"ower, Gang Wang, Jian Sun, Wenjie Liu, Yifan Xie, Yuzhou Wei","submitted_at":"2026-06-23T08:54:22Z","abstract_excerpt":"Data-driven model predictive control (MPC) has become an attractive approach for controlling unknown systems, especially when data are corrupted by noise. However, most existing data-driven MPC methods focus on linear systems, and little attention has been given to nonlinear dynamics under disturbances. To fill this gap, we propose a robust data-driven min-max MPC scheme for unknown nonlinear systems with process disturbances. We represent the unknown nonlinear dynamics using vector fields built from a dictionary of basis functions, yielding an equivalent linear form with unknown matrices. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24316","kind":"arxiv","version":1},"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/2606.24316/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":"2606.24316","created_at":"2026-06-24T01:14:49.779905+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24316v1","created_at":"2026-06-24T01:14:49.779905+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24316","created_at":"2026-06-24T01:14:49.779905+00:00"},{"alias_kind":"pith_short_12","alias_value":"AIVWKI3VDHOL","created_at":"2026-06-24T01:14:49.779905+00:00"},{"alias_kind":"pith_short_16","alias_value":"AIVWKI3VDHOL2KYI","created_at":"2026-06-24T01:14:49.779905+00:00"},{"alias_kind":"pith_short_8","alias_value":"AIVWKI3V","created_at":"2026-06-24T01:14:49.779905+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM","json":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM.json","graph_json":"https://pith.science/api/pith-number/AIVWKI3VDHOL2KYIMQYY5USREM/graph.json","events_json":"https://pith.science/api/pith-number/AIVWKI3VDHOL2KYIMQYY5USREM/events.json","paper":"https://pith.science/paper/AIVWKI3V"},"agent_actions":{"view_html":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM","download_json":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM.json","view_paper":"https://pith.science/paper/AIVWKI3V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24316&json=true","fetch_graph":"https://pith.science/api/pith-number/AIVWKI3VDHOL2KYIMQYY5USREM/graph.json","fetch_events":"https://pith.science/api/pith-number/AIVWKI3VDHOL2KYIMQYY5USREM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM/action/storage_attestation","attest_author":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM/action/author_attestation","sign_citation":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM/action/citation_signature","submit_replication":"https://pith.science/pith/AIVWKI3VDHOL2KYIMQYY5USREM/action/replication_record"}},"created_at":"2026-06-24T01:14:49.779905+00:00","updated_at":"2026-06-24T01:14:49.779905+00:00"}