{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:YSPRF2MIHXKGKIXBLY64ZVX7H7","short_pith_number":"pith:YSPRF2MI","schema_version":"1.0","canonical_sha256":"c49f12e9883dd46522e15e3dccd6ff3fe8dbd1d2c67c5065f84f47ca5a5da634","source":{"kind":"arxiv","id":"1301.0931","version":1},"attestation_state":"computed","paper":{"title":"LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Amitava Gupta, Indranil Pan, Kaushik Halder, Saptarshi Das, Shantanu Das","submitted_at":"2013-01-05T18:56:45Z","abstract_excerpt":"The continuous and discrete time Linear Quadratic Regulator (LQR) theory has been used in this paper for the design of optimal analog and discrete PID controllers respectively. The PID controller gains are formulated as the optimal state-feedback gains, corresponding to the standard quadratic cost function involving the state variables and the controller effort. A real coded Genetic Algorithm (GA) has been used next to optimally find out the weighting matrices, associated with the respective optimal state-feedback regulator design while minimizing another time domain integral performance index"},"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":"1301.0931","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2013-01-05T18:56:45Z","cross_cats_sorted":[],"title_canon_sha256":"d130ae5321a42887b5ab85b14ad3f4f5f60cb7debc10a7d0db62cc34b4004ffe","abstract_canon_sha256":"f3d33df65453ad6118d61dc5c5e8ea61e67f9370e52d3bedc1e4ba1d5a4cdfb4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:36:15.596521Z","signature_b64":"XhvfyZJwrrpuK0aC84ul9F4UCQQWiI8+i/ZAcPWCFwz3+xzjOnKeCerY6ekwdhPaRQTK1jTAQlfxu+1PKdsRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c49f12e9883dd46522e15e3dccd6ff3fe8dbd1d2c67c5065f84f47ca5a5da634","last_reissued_at":"2026-05-18T03:36:15.595924Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:36:15.595924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Amitava Gupta, Indranil Pan, Kaushik Halder, Saptarshi Das, Shantanu Das","submitted_at":"2013-01-05T18:56:45Z","abstract_excerpt":"The continuous and discrete time Linear Quadratic Regulator (LQR) theory has been used in this paper for the design of optimal analog and discrete PID controllers respectively. The PID controller gains are formulated as the optimal state-feedback gains, corresponding to the standard quadratic cost function involving the state variables and the controller effort. A real coded Genetic Algorithm (GA) has been used next to optimally find out the weighting matrices, associated with the respective optimal state-feedback regulator design while minimizing another time domain integral performance index"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.0931","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1301.0931","created_at":"2026-05-18T03:36:15.596055+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.0931v1","created_at":"2026-05-18T03:36:15.596055+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.0931","created_at":"2026-05-18T03:36:15.596055+00:00"},{"alias_kind":"pith_short_12","alias_value":"YSPRF2MIHXKG","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_16","alias_value":"YSPRF2MIHXKGKIXB","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_8","alias_value":"YSPRF2MI","created_at":"2026-05-18T12:28:09.283467+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/YSPRF2MIHXKGKIXBLY64ZVX7H7","json":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7.json","graph_json":"https://pith.science/api/pith-number/YSPRF2MIHXKGKIXBLY64ZVX7H7/graph.json","events_json":"https://pith.science/api/pith-number/YSPRF2MIHXKGKIXBLY64ZVX7H7/events.json","paper":"https://pith.science/paper/YSPRF2MI"},"agent_actions":{"view_html":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7","download_json":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7.json","view_paper":"https://pith.science/paper/YSPRF2MI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.0931&json=true","fetch_graph":"https://pith.science/api/pith-number/YSPRF2MIHXKGKIXBLY64ZVX7H7/graph.json","fetch_events":"https://pith.science/api/pith-number/YSPRF2MIHXKGKIXBLY64ZVX7H7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7/action/storage_attestation","attest_author":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7/action/author_attestation","sign_citation":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7/action/citation_signature","submit_replication":"https://pith.science/pith/YSPRF2MIHXKGKIXBLY64ZVX7H7/action/replication_record"}},"created_at":"2026-05-18T03:36:15.596055+00:00","updated_at":"2026-05-18T03:36:15.596055+00:00"}