{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:UTWSLH23FPU65Z5EI63RKNFNN6","short_pith_number":"pith:UTWSLH23","schema_version":"1.0","canonical_sha256":"a4ed259f5b2be9eee7a447b71534ad6fab6507661faa0b69f95c9c48b25e52be","source":{"kind":"arxiv","id":"2204.02142","version":2},"attestation_state":"computed","paper":{"title":"Computationally efficient robust MPC using optimized constraint tightening","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Andrea Iannelli, Anilkumar Parsi, Panagiotis Anagnostaras, Roy S. Smith","submitted_at":"2022-04-05T12:06:16Z","abstract_excerpt":"A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline design of a disturbance-affine feedback gain whereby the resulting constraint tightening is minimized. This is achieved by formulating the constraint tightening problem as a convex optimization problem with the feedback term as a variable. The resulting MPC controller has the computational complexity of nominal MPC, and guarantees recursive feasibility, stability and constraint satisfaction. The advantages of the proposed"},"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":"2204.02142","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2022-04-05T12:06:16Z","cross_cats_sorted":["cs.SY","math.OC"],"title_canon_sha256":"d67e27b3658ce4df61df3ac9d7fa9947b1fef249ffa9098ed8fff1f35c80b6ae","abstract_canon_sha256":"215cd9ce80266e9645c75c7141b6f3bbed430340d7fd2e76c19d22c63d981960"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:59.798459Z","signature_b64":"x25RAnkxWWein0WIlgWJtT1UJtzXY/ECE40clRgR3RQGTJlarmbQsN2+JfLO8GiFDW7ErwCMLnlkUgLpc64EAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4ed259f5b2be9eee7a447b71534ad6fab6507661faa0b69f95c9c48b25e52be","last_reissued_at":"2026-07-05T05:15:59.797955Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:59.797955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computationally efficient robust MPC using optimized constraint tightening","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Andrea Iannelli, Anilkumar Parsi, Panagiotis Anagnostaras, Roy S. Smith","submitted_at":"2022-04-05T12:06:16Z","abstract_excerpt":"A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline design of a disturbance-affine feedback gain whereby the resulting constraint tightening is minimized. This is achieved by formulating the constraint tightening problem as a convex optimization problem with the feedback term as a variable. The resulting MPC controller has the computational complexity of nominal MPC, and guarantees recursive feasibility, stability and constraint satisfaction. The advantages of the proposed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.02142","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/2204.02142/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":"2204.02142","created_at":"2026-07-05T05:15:59.798020+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.02142v2","created_at":"2026-07-05T05:15:59.798020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.02142","created_at":"2026-07-05T05:15:59.798020+00:00"},{"alias_kind":"pith_short_12","alias_value":"UTWSLH23FPU6","created_at":"2026-07-05T05:15:59.798020+00:00"},{"alias_kind":"pith_short_16","alias_value":"UTWSLH23FPU65Z5E","created_at":"2026-07-05T05:15:59.798020+00:00"},{"alias_kind":"pith_short_8","alias_value":"UTWSLH23","created_at":"2026-07-05T05:15:59.798020+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/UTWSLH23FPU65Z5EI63RKNFNN6","json":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6.json","graph_json":"https://pith.science/api/pith-number/UTWSLH23FPU65Z5EI63RKNFNN6/graph.json","events_json":"https://pith.science/api/pith-number/UTWSLH23FPU65Z5EI63RKNFNN6/events.json","paper":"https://pith.science/paper/UTWSLH23"},"agent_actions":{"view_html":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6","download_json":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6.json","view_paper":"https://pith.science/paper/UTWSLH23","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.02142&json=true","fetch_graph":"https://pith.science/api/pith-number/UTWSLH23FPU65Z5EI63RKNFNN6/graph.json","fetch_events":"https://pith.science/api/pith-number/UTWSLH23FPU65Z5EI63RKNFNN6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6/action/storage_attestation","attest_author":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6/action/author_attestation","sign_citation":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6/action/citation_signature","submit_replication":"https://pith.science/pith/UTWSLH23FPU65Z5EI63RKNFNN6/action/replication_record"}},"created_at":"2026-07-05T05:15:59.798020+00:00","updated_at":"2026-07-05T05:15:59.798020+00:00"}