{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LQVZ63B3HJXYDDOVC7ZGYF4QOZ","short_pith_number":"pith:LQVZ63B3","schema_version":"1.0","canonical_sha256":"5c2b9f6c3b3a6f818dd517f26c1790765f38bcb7a487a245ce3ae30845d472a9","source":{"kind":"arxiv","id":"2102.12124","version":3},"attestation_state":"computed","paper":{"title":"Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Hui Cheng, Jiesen Pan, Lei Zheng, Rui Yang, Zhixuan Wu","submitted_at":"2021-02-24T08:39:01Z","abstract_excerpt":"In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a non-differentiable objective functi"},"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":"2102.12124","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-02-24T08:39:01Z","cross_cats_sorted":[],"title_canon_sha256":"4755f48942718e6dfb6e4da65aaea6f13b77363bb686f57e47acda62824d6a4d","abstract_canon_sha256":"cf7c1cc80712f87815431835357c3452cc9e5da9fb731250bc3b2805d5eb7927"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:58:14.468232Z","signature_b64":"yRPpJCs2DRDcTf1R1pl2w1y0PgbRhSYAAduojAVMvLlIfvjxc+V3ZxMCmY8NbQIKqk3KTvGM1jD9kHR0xjfuCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c2b9f6c3b3a6f818dd517f26c1790765f38bcb7a487a245ce3ae30845d472a9","last_reissued_at":"2026-07-05T03:58:14.467758Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:58:14.467758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Hui Cheng, Jiesen Pan, Lei Zheng, Rui Yang, Zhixuan Wu","submitted_at":"2021-02-24T08:39:01Z","abstract_excerpt":"In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a non-differentiable objective functi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12124","kind":"arxiv","version":3},"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/2102.12124/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":"2102.12124","created_at":"2026-07-05T03:58:14.467816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.12124v3","created_at":"2026-07-05T03:58:14.467816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12124","created_at":"2026-07-05T03:58:14.467816+00:00"},{"alias_kind":"pith_short_12","alias_value":"LQVZ63B3HJXY","created_at":"2026-07-05T03:58:14.467816+00:00"},{"alias_kind":"pith_short_16","alias_value":"LQVZ63B3HJXYDDOV","created_at":"2026-07-05T03:58:14.467816+00:00"},{"alias_kind":"pith_short_8","alias_value":"LQVZ63B3","created_at":"2026-07-05T03:58:14.467816+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/LQVZ63B3HJXYDDOVC7ZGYF4QOZ","json":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ.json","graph_json":"https://pith.science/api/pith-number/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/graph.json","events_json":"https://pith.science/api/pith-number/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/events.json","paper":"https://pith.science/paper/LQVZ63B3"},"agent_actions":{"view_html":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ","download_json":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ.json","view_paper":"https://pith.science/paper/LQVZ63B3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.12124&json=true","fetch_graph":"https://pith.science/api/pith-number/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/graph.json","fetch_events":"https://pith.science/api/pith-number/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/action/storage_attestation","attest_author":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/action/author_attestation","sign_citation":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/action/citation_signature","submit_replication":"https://pith.science/pith/LQVZ63B3HJXYDDOVC7ZGYF4QOZ/action/replication_record"}},"created_at":"2026-07-05T03:58:14.467816+00:00","updated_at":"2026-07-05T03:58:14.467816+00:00"}