{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GP67EDMLJ3GCI74AXBCCCZL6XB","short_pith_number":"pith:GP67EDML","schema_version":"1.0","canonical_sha256":"33fdf20d8b4ecc247f80b84421657eb86819237b4d1512c703c43f53995d7d90","source":{"kind":"arxiv","id":"2509.11235","version":1},"attestation_state":"computed","paper":{"title":"Comparing Model-based Control Strategies for a Quadruple Tank System: Decentralized PID, LMPC, and NMPC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Anders H. D. Christensen, Jakob Kj{\\o}bsted Huusom, Jan Lorenz Svensen, John Bagterp J{\\o}rgensen, Steen H{\\o}rsholt, Tobias K. S. Ritschel","submitted_at":"2025-09-14T12:27:07Z","abstract_excerpt":"This paper compares the performance of a decentralized proportional-integral-derivative (PID) controller, a linear model predictive controller (LMPC), and a nonlinear model predictive controller (NMPC) applied to a quadruple tank system (QTS). We present experimental data from a physical setup of the QTS as well as simulation results. The QTS is modeled as a stochastic nonlinear continuous-discrete-time system, with parameters estimated using a maximum-likelihood prediction-error-method (ML-PEM). The NMPC applies the stochastic nonlinear continuous-discrete-time model, while the LMPC uses a li"},"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":"2509.11235","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2025-09-14T12:27:07Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"27399bca97801f6f9da949b3f94539d69f133e30c5c26b4f7d99c0a5b6a7c962","abstract_canon_sha256":"c805a2eb4eef53f138ab9aa74f0df3a6c69abf4808522dbe685ef4107b23fde8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:35.812251Z","signature_b64":"EDg6kZVDDQNl33yCrcICqXh2kodP+WgqPy40AvfWFyuTAhd9S868PkWd5+WAP3ZplG4KYpnnZMPE3bsDcKvpCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33fdf20d8b4ecc247f80b84421657eb86819237b4d1512c703c43f53995d7d90","last_reissued_at":"2026-07-05T12:11:35.811772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:35.811772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparing Model-based Control Strategies for a Quadruple Tank System: Decentralized PID, LMPC, and NMPC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Anders H. D. Christensen, Jakob Kj{\\o}bsted Huusom, Jan Lorenz Svensen, John Bagterp J{\\o}rgensen, Steen H{\\o}rsholt, Tobias K. S. Ritschel","submitted_at":"2025-09-14T12:27:07Z","abstract_excerpt":"This paper compares the performance of a decentralized proportional-integral-derivative (PID) controller, a linear model predictive controller (LMPC), and a nonlinear model predictive controller (NMPC) applied to a quadruple tank system (QTS). We present experimental data from a physical setup of the QTS as well as simulation results. The QTS is modeled as a stochastic nonlinear continuous-discrete-time system, with parameters estimated using a maximum-likelihood prediction-error-method (ML-PEM). The NMPC applies the stochastic nonlinear continuous-discrete-time model, while the LMPC uses a li"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.11235","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.11235/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":"2509.11235","created_at":"2026-07-05T12:11:35.811825+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.11235v1","created_at":"2026-07-05T12:11:35.811825+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.11235","created_at":"2026-07-05T12:11:35.811825+00:00"},{"alias_kind":"pith_short_12","alias_value":"GP67EDMLJ3GC","created_at":"2026-07-05T12:11:35.811825+00:00"},{"alias_kind":"pith_short_16","alias_value":"GP67EDMLJ3GCI74A","created_at":"2026-07-05T12:11:35.811825+00:00"},{"alias_kind":"pith_short_8","alias_value":"GP67EDML","created_at":"2026-07-05T12:11:35.811825+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10431","citing_title":"Hierarchical 2-degree-of-freedom control combining Youla-Kucera parameterization and model predictive control","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB","json":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB.json","graph_json":"https://pith.science/api/pith-number/GP67EDMLJ3GCI74AXBCCCZL6XB/graph.json","events_json":"https://pith.science/api/pith-number/GP67EDMLJ3GCI74AXBCCCZL6XB/events.json","paper":"https://pith.science/paper/GP67EDML"},"agent_actions":{"view_html":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB","download_json":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB.json","view_paper":"https://pith.science/paper/GP67EDML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.11235&json=true","fetch_graph":"https://pith.science/api/pith-number/GP67EDMLJ3GCI74AXBCCCZL6XB/graph.json","fetch_events":"https://pith.science/api/pith-number/GP67EDMLJ3GCI74AXBCCCZL6XB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB/action/storage_attestation","attest_author":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB/action/author_attestation","sign_citation":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB/action/citation_signature","submit_replication":"https://pith.science/pith/GP67EDMLJ3GCI74AXBCCCZL6XB/action/replication_record"}},"created_at":"2026-07-05T12:11:35.811825+00:00","updated_at":"2026-07-05T12:11:35.811825+00:00"}