{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R27ZE45NCDLK75VGULFTJ5NJRZ","short_pith_number":"pith:R27ZE45N","schema_version":"1.0","canonical_sha256":"8ebf9273ad10d6aff6a6a2cb34f5a98e72f3253973fd36ab309cc7f5672cb7f6","source":{"kind":"arxiv","id":"2603.17843","version":2},"attestation_state":"computed","paper":{"title":"Certainty-equivalent adaptive MPC for uncertain nonlinear systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Johannes K\\\"ohler","submitted_at":"2026-03-18T15:33:46Z","abstract_excerpt":"We provide a method to design adaptive controllers for nonlinear systems using model predictive control (MPC). By combining a certainty-equivalent MPC formulation with least-mean-square parameter adaptation, we obtain an adaptive controller with strong robust performance guarantees: The cumulative tracking error and violation of state constraints scale linearly with noise energy, disturbance energy, and path length of parameter variation. A key technical contribution is developing the underlying certainty-equivalent MPC that tracks output references, accounts for actuator limitations and desir"},"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":"2603.17843","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-03-18T15:33:46Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"334dada2417c37439613c4a0db58eec1dadc4ce0eb7f0c29ade11f99f2833609","abstract_canon_sha256":"b404f323ab26c73b9e80d30b6c97fd830e6012e5ae0af89c49248c65f07875cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:21:36.966884Z","signature_b64":"KGOUlyXSrNBKtBZOg4HqXKTESpc0fbmUBz+54fr+i3qDe6DUMqCQXqGW0MSJKWHlGw7ik11i/rL8XH3pIXv7CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ebf9273ad10d6aff6a6a2cb34f5a98e72f3253973fd36ab309cc7f5672cb7f6","last_reissued_at":"2026-07-28T01:21:36.965963Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:21:36.965963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Certainty-equivalent adaptive MPC for uncertain nonlinear systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Johannes K\\\"ohler","submitted_at":"2026-03-18T15:33:46Z","abstract_excerpt":"We provide a method to design adaptive controllers for nonlinear systems using model predictive control (MPC). By combining a certainty-equivalent MPC formulation with least-mean-square parameter adaptation, we obtain an adaptive controller with strong robust performance guarantees: The cumulative tracking error and violation of state constraints scale linearly with noise energy, disturbance energy, and path length of parameter variation. A key technical contribution is developing the underlying certainty-equivalent MPC that tracks output references, accounts for actuator limitations and desir"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.17843","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/2603.17843/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":"2603.17843","created_at":"2026-07-28T01:21:36.966392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.17843v2","created_at":"2026-07-28T01:21:36.966392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.17843","created_at":"2026-07-28T01:21:36.966392+00:00"},{"alias_kind":"pith_short_12","alias_value":"R27ZE45NCDLK","created_at":"2026-07-28T01:21:36.966392+00:00"},{"alias_kind":"pith_short_16","alias_value":"R27ZE45NCDLK75VG","created_at":"2026-07-28T01:21:36.966392+00:00"},{"alias_kind":"pith_short_8","alias_value":"R27ZE45N","created_at":"2026-07-28T01:21:36.966392+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.04656","citing_title":"Adaptive MPC for Constrained Trajectory Tracking of Uncertain LTI System with Input-Rate Limits","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ","json":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ.json","graph_json":"https://pith.science/api/pith-number/R27ZE45NCDLK75VGULFTJ5NJRZ/graph.json","events_json":"https://pith.science/api/pith-number/R27ZE45NCDLK75VGULFTJ5NJRZ/events.json","paper":"https://pith.science/paper/R27ZE45N"},"agent_actions":{"view_html":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ","download_json":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ.json","view_paper":"https://pith.science/paper/R27ZE45N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.17843&json=true","fetch_graph":"https://pith.science/api/pith-number/R27ZE45NCDLK75VGULFTJ5NJRZ/graph.json","fetch_events":"https://pith.science/api/pith-number/R27ZE45NCDLK75VGULFTJ5NJRZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ/action/storage_attestation","attest_author":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ/action/author_attestation","sign_citation":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ/action/citation_signature","submit_replication":"https://pith.science/pith/R27ZE45NCDLK75VGULFTJ5NJRZ/action/replication_record"}},"created_at":"2026-07-28T01:21:36.966392+00:00","updated_at":"2026-07-28T01:21:36.966392+00:00"}