{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HCVMCGTCAQ6ELHYJ2H5ZFYNIUW","short_pith_number":"pith:HCVMCGTC","canonical_record":{"source":{"id":"1810.12584","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2018-10-30T08:48:14Z","cross_cats_sorted":[],"title_canon_sha256":"440137095a8466c74ad5db1437492cbf81ebdfd723011c7684e9557841f354f2","abstract_canon_sha256":"c57f620e1352c4e114c9c922a3233ef0b40ca6b2170635e882a5e1771c378a51"},"schema_version":"1.0"},"canonical_sha256":"38aac11a62043c459f09d1fb92e1a8a58842fec6764c120235a1112fa8fa64c8","source":{"kind":"arxiv","id":"1810.12584","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12584","created_at":"2026-05-18T00:01:56Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12584v1","created_at":"2026-05-18T00:01:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12584","created_at":"2026-05-18T00:01:56Z"},{"alias_kind":"pith_short_12","alias_value":"HCVMCGTCAQ6E","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HCVMCGTCAQ6ELHYJ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HCVMCGTC","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HCVMCGTCAQ6ELHYJ2H5ZFYNIUW","target":"record","payload":{"canonical_record":{"source":{"id":"1810.12584","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2018-10-30T08:48:14Z","cross_cats_sorted":[],"title_canon_sha256":"440137095a8466c74ad5db1437492cbf81ebdfd723011c7684e9557841f354f2","abstract_canon_sha256":"c57f620e1352c4e114c9c922a3233ef0b40ca6b2170635e882a5e1771c378a51"},"schema_version":"1.0"},"canonical_sha256":"38aac11a62043c459f09d1fb92e1a8a58842fec6764c120235a1112fa8fa64c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:56.594915Z","signature_b64":"edGzrBsSkGzcIguXkzDM9Tp5ufLHOEgWYExRTgwc6M8CKVzX+H0FBvFTCurFkpca979TYZ978N5vlEK1aByqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38aac11a62043c459f09d1fb92e1a8a58842fec6764c120235a1112fa8fa64c8","last_reissued_at":"2026-05-18T00:01:56.594466Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:56.594466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.12584","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9FYAX1NKhx1jwG3N+QojKCOhirc+N1t8WueLZTiNGTBXBDKo6dsoRjqbTm+4tAqUgdk+kLO9L/BjUS29qrAAAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T01:29:04.906118Z"},"content_sha256":"c191f64d1be57ce66a5f5bf181de5f0f528ea651d55df81293b46ea5cfd2d29c","schema_version":"1.0","event_id":"sha256:c191f64d1be57ce66a5f5bf181de5f0f528ea651d55df81293b46ea5cfd2d29c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HCVMCGTCAQ6ELHYJ2H5ZFYNIUW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning-based predictive control for linear systems: a unitary approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Enrico Terzi, Lorenzo Fagiano, Marcello Farina, Riccardo Scattolini","submitted_at":"2018-10-30T08:48:14Z","abstract_excerpt":"A comprehensive approach addressing identification and control for learningbased Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-driven MPC law, based on a dataset collected from the working plant. The method is indirect, i.e. it relies on a model learning phase and a model-based control design one, devised in an integrated manner. In the model learning phase, a twofold outcome is achieved: first, different optimal p-steps ahead prediction models are obtained, to be used in the MPC cost function; secondly, a perturbed state-space model is deri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12584","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aqNC4ba2h5xj1kyxuYUZkVjnIcsX1HgIKYqQXl8r+zXvDbVZpRU1RgXHqVraV61mt/2x2NPpTgnnKqkK7klcDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T01:29:04.906478Z"},"content_sha256":"446cb6d9a191c0680f0c7be2b92cd92fd91338eeb081a1ef14f1562319682246","schema_version":"1.0","event_id":"sha256:446cb6d9a191c0680f0c7be2b92cd92fd91338eeb081a1ef14f1562319682246"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW/bundle.json","state_url":"https://pith.science/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-22T01:29:04Z","links":{"resolver":"https://pith.science/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW","bundle":"https://pith.science/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW/bundle.json","state":"https://pith.science/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HCVMCGTCAQ6ELHYJ2H5ZFYNIUW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HCVMCGTCAQ6ELHYJ2H5ZFYNIUW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c57f620e1352c4e114c9c922a3233ef0b40ca6b2170635e882a5e1771c378a51","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2018-10-30T08:48:14Z","title_canon_sha256":"440137095a8466c74ad5db1437492cbf81ebdfd723011c7684e9557841f354f2"},"schema_version":"1.0","source":{"id":"1810.12584","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12584","created_at":"2026-05-18T00:01:56Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12584v1","created_at":"2026-05-18T00:01:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12584","created_at":"2026-05-18T00:01:56Z"},{"alias_kind":"pith_short_12","alias_value":"HCVMCGTCAQ6E","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HCVMCGTCAQ6ELHYJ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HCVMCGTC","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:446cb6d9a191c0680f0c7be2b92cd92fd91338eeb081a1ef14f1562319682246","target":"graph","created_at":"2026-05-18T00:01:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"A comprehensive approach addressing identification and control for learningbased Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-driven MPC law, based on a dataset collected from the working plant. The method is indirect, i.e. it relies on a model learning phase and a model-based control design one, devised in an integrated manner. In the model learning phase, a twofold outcome is achieved: first, different optimal p-steps ahead prediction models are obtained, to be used in the MPC cost function; secondly, a perturbed state-space model is deri","authors_text":"Enrico Terzi, Lorenzo Fagiano, Marcello Farina, Riccardo Scattolini","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2018-10-30T08:48:14Z","title":"Learning-based predictive control for linear systems: a unitary approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12584","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c191f64d1be57ce66a5f5bf181de5f0f528ea651d55df81293b46ea5cfd2d29c","target":"record","created_at":"2026-05-18T00:01:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c57f620e1352c4e114c9c922a3233ef0b40ca6b2170635e882a5e1771c378a51","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2018-10-30T08:48:14Z","title_canon_sha256":"440137095a8466c74ad5db1437492cbf81ebdfd723011c7684e9557841f354f2"},"schema_version":"1.0","source":{"id":"1810.12584","kind":"arxiv","version":1}},"canonical_sha256":"38aac11a62043c459f09d1fb92e1a8a58842fec6764c120235a1112fa8fa64c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38aac11a62043c459f09d1fb92e1a8a58842fec6764c120235a1112fa8fa64c8","first_computed_at":"2026-05-18T00:01:56.594466Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:56.594466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"edGzrBsSkGzcIguXkzDM9Tp5ufLHOEgWYExRTgwc6M8CKVzX+H0FBvFTCurFkpca979TYZ978N5vlEK1aByqAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:56.594915Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.12584","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c191f64d1be57ce66a5f5bf181de5f0f528ea651d55df81293b46ea5cfd2d29c","sha256:446cb6d9a191c0680f0c7be2b92cd92fd91338eeb081a1ef14f1562319682246"],"state_sha256":"da9583ec18bf8d26dc735048549ab93379c45dd32068644eb0b304a7a4d59938"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ctWlubyCe++8c26s+CrP10mrrjURBj60/KxCuqEudcnXyTI5ffc9vUtJt7/1ISEDd0xDnnJuPcIEiVa9VPcUAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T01:29:04.908444Z","bundle_sha256":"fb1385944d146b15fbd2799b816a37502d1255fb75db0a10a6aa4d2ea2eb56cd"}}