{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UWWOUVLFEON3YTDS2D7UMWWPHB","short_pith_number":"pith:UWWOUVLF","schema_version":"1.0","canonical_sha256":"a5acea5565239bbc4c72d0ff465acf386c533f844f06267f57333467ffad2b60","source":{"kind":"arxiv","id":"1802.09767","version":1},"attestation_state":"computed","paper":{"title":"On multi-step prediction models for receding horizon control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS","math.OC"],"primary_cat":"cs.SY","authors_text":"Enrico Terzi, Lorenzo Fagiano, Marcello Farina, Riccardo Scattolini","submitted_at":"2018-02-27T08:25:01Z","abstract_excerpt":"The derivation of multi-step-ahead prediction models from sampled data of a linear system is considered. A dedicated prediction model is built for each future time step of interest. In addition to a nominal model, the set of all models consistent with data and prior information is derived as well, making the approach suitable for robust control design within a Model Predictive Control framework. The resulting parameter identification problem is solved through a sequence of convex programs, overcoming the non-convexity arising when identifying 1-step prediction models with an output-error crite"},"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":"1802.09767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2018-02-27T08:25:01Z","cross_cats_sorted":["math.DS","math.OC"],"title_canon_sha256":"3dc5117bcee984b8ff3ab8336bef5701a426697ed4db4998d0e929cddadf1346","abstract_canon_sha256":"d93590985134cc9ee3e5ea6dd8d0e69588ee1a88f9933fce6fb0958581c594a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:22.293868Z","signature_b64":"Qa5H49eqgpuMK3oclnNn2N3jOdFSSCm4AePyIn5hpjP9XjbkwqJaL3mQn4OurZcxuf7v5bupYdqMSxuIKqlwDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5acea5565239bbc4c72d0ff465acf386c533f844f06267f57333467ffad2b60","last_reissued_at":"2026-05-18T00:22:22.293224Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:22.293224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On multi-step prediction models for receding horizon control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS","math.OC"],"primary_cat":"cs.SY","authors_text":"Enrico Terzi, Lorenzo Fagiano, Marcello Farina, Riccardo Scattolini","submitted_at":"2018-02-27T08:25:01Z","abstract_excerpt":"The derivation of multi-step-ahead prediction models from sampled data of a linear system is considered. A dedicated prediction model is built for each future time step of interest. In addition to a nominal model, the set of all models consistent with data and prior information is derived as well, making the approach suitable for robust control design within a Model Predictive Control framework. The resulting parameter identification problem is solved through a sequence of convex programs, overcoming the non-convexity arising when identifying 1-step prediction models with an output-error crite"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.09767","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":""},"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":"1802.09767","created_at":"2026-05-18T00:22:22.293326+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.09767v1","created_at":"2026-05-18T00:22:22.293326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.09767","created_at":"2026-05-18T00:22:22.293326+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWWOUVLFEON3","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWWOUVLFEON3YTDS","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWWOUVLF","created_at":"2026-05-18T12:32:56.356000+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/UWWOUVLFEON3YTDS2D7UMWWPHB","json":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB.json","graph_json":"https://pith.science/api/pith-number/UWWOUVLFEON3YTDS2D7UMWWPHB/graph.json","events_json":"https://pith.science/api/pith-number/UWWOUVLFEON3YTDS2D7UMWWPHB/events.json","paper":"https://pith.science/paper/UWWOUVLF"},"agent_actions":{"view_html":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB","download_json":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB.json","view_paper":"https://pith.science/paper/UWWOUVLF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.09767&json=true","fetch_graph":"https://pith.science/api/pith-number/UWWOUVLFEON3YTDS2D7UMWWPHB/graph.json","fetch_events":"https://pith.science/api/pith-number/UWWOUVLFEON3YTDS2D7UMWWPHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB/action/storage_attestation","attest_author":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB/action/author_attestation","sign_citation":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB/action/citation_signature","submit_replication":"https://pith.science/pith/UWWOUVLFEON3YTDS2D7UMWWPHB/action/replication_record"}},"created_at":"2026-05-18T00:22:22.293326+00:00","updated_at":"2026-05-18T00:22:22.293326+00:00"}