{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NZL6IYDLQJTVSMW73E6TLEKQMP","short_pith_number":"pith:NZL6IYDL","schema_version":"1.0","canonical_sha256":"6e57e4606b82675932dfd93d35915063e3eac304f8fad7562f0d21ad23047c80","source":{"kind":"arxiv","id":"1909.07239","version":5},"attestation_state":"computed","paper":{"title":"Adaptive Dynamic Programming for Model-free Tracking of Trajectories with Time-varying Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO","cs.SY"],"primary_cat":"eess.SY","authors_text":"Florian K\\\"opf, Michael Flad, Simon Ramsteiner, S\\\"oren Hohmann","submitted_at":"2019-09-16T14:37:31Z","abstract_excerpt":"In order to autonomously learn to control unknown systems optimally w.r.t. an objective function, Adaptive Dynamic Programming (ADP) is well-suited to adapt controllers based on experience from interaction with the system. In recent years, many researchers focused on the tracking case, where the aim is to follow a desired trajectory. So far, ADP tracking controllers assume that the reference trajectory follows time-invariant exo-system dynamics-an assumption that does not hold for many applications. In order to overcome this limitation, we propose a new Q-function which explicitly incorporates"},"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":"1909.07239","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2019-09-16T14:37:31Z","cross_cats_sorted":["cs.LG","cs.RO","cs.SY"],"title_canon_sha256":"96b950877608f4d8a7eab60764b81427ec3c7e74882cc80b392a76e83aa64f3b","abstract_canon_sha256":"ae495fc584525f9791f69cf82b53b5f219995edbcee91ce81acf58a5a111add6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:40:58.481244Z","signature_b64":"w/lMIL0n9ADtByIOYnboC3XIQPoD5SYY3L5G28mwK4UDdBJvBu97t4FeAe3QATRf19HgnrTOD554Vu6k3wvyAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e57e4606b82675932dfd93d35915063e3eac304f8fad7562f0d21ad23047c80","last_reissued_at":"2026-07-05T00:40:58.480893Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:40:58.480893Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Dynamic Programming for Model-free Tracking of Trajectories with Time-varying Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO","cs.SY"],"primary_cat":"eess.SY","authors_text":"Florian K\\\"opf, Michael Flad, Simon Ramsteiner, S\\\"oren Hohmann","submitted_at":"2019-09-16T14:37:31Z","abstract_excerpt":"In order to autonomously learn to control unknown systems optimally w.r.t. an objective function, Adaptive Dynamic Programming (ADP) is well-suited to adapt controllers based on experience from interaction with the system. In recent years, many researchers focused on the tracking case, where the aim is to follow a desired trajectory. So far, ADP tracking controllers assume that the reference trajectory follows time-invariant exo-system dynamics-an assumption that does not hold for many applications. In order to overcome this limitation, we propose a new Q-function which explicitly incorporates"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.07239","kind":"arxiv","version":5},"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/1909.07239/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":"1909.07239","created_at":"2026-07-05T00:40:58.480957+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.07239v5","created_at":"2026-07-05T00:40:58.480957+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.07239","created_at":"2026-07-05T00:40:58.480957+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZL6IYDLQJTV","created_at":"2026-07-05T00:40:58.480957+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZL6IYDLQJTVSMW7","created_at":"2026-07-05T00:40:58.480957+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZL6IYDL","created_at":"2026-07-05T00:40:58.480957+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/NZL6IYDLQJTVSMW73E6TLEKQMP","json":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP.json","graph_json":"https://pith.science/api/pith-number/NZL6IYDLQJTVSMW73E6TLEKQMP/graph.json","events_json":"https://pith.science/api/pith-number/NZL6IYDLQJTVSMW73E6TLEKQMP/events.json","paper":"https://pith.science/paper/NZL6IYDL"},"agent_actions":{"view_html":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP","download_json":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP.json","view_paper":"https://pith.science/paper/NZL6IYDL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.07239&json=true","fetch_graph":"https://pith.science/api/pith-number/NZL6IYDLQJTVSMW73E6TLEKQMP/graph.json","fetch_events":"https://pith.science/api/pith-number/NZL6IYDLQJTVSMW73E6TLEKQMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP/action/storage_attestation","attest_author":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP/action/author_attestation","sign_citation":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP/action/citation_signature","submit_replication":"https://pith.science/pith/NZL6IYDLQJTVSMW73E6TLEKQMP/action/replication_record"}},"created_at":"2026-07-05T00:40:58.480957+00:00","updated_at":"2026-07-05T00:40:58.480957+00:00"}