[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82793-en":3,"doc-seo-82793-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82793,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Scenario-Based Data-Enabled Predictive Control Robustification via the Scenario Approach","Scenario-Based Data-Enabled Predictive Control (Scenario-DeePC) integrates the scenario optimization framework into Data-enabled Predictive Control (DeePC) to deliver probabilistic guarantees for constraint satisfaction under uncertainty. Uncertainty is inferred directly from data by building empirical disturbance scenarios from observed prediction errors, avoiding distributional assumptions. The work provides supporting theory, including distribution-free probabilistic constraint guarantees and recursive feasibility for the receding-horizon scheme. An adaptive variant gathers scenarios online to track changing noise, disturbances, and model mismatch dependent on operating points, validated on linear and nonlinear systems.","arXiv :2607 .04165v1 [ ee ss . SY] 5 Jul 2026  \n RESEARCH ARTICLE  OPEN ACCESS   \nScenario-based Data-Enabled Predictive Control: Robustification via the Scenario Approach  \nSebastian Zieglmeier  | Nikolas Recke  | Mathias Hudoba de Badyn   \n1 Department of Technology Systems, University of Oslo, Oslo, Norway |  \nCorrespondence: Sebastian Zieglmeier ([sebastiz@uio.no](sebastiz@uio.no)) | Nikolas Recke ([nikolalr@uio.no](nikolalr@uio.no)) | Mathias Hudoba de Badyn ([mathihud@uio.no](mathihud@uio.no))  \nFunding: IDES (Intelligent Dynamic Energy Systems) project; FME Solar, Grant/Award Number: 350244  \nKeywords: data-enabled predictive control | scenario approach | probabilistic constraint satisfaction | data-driven control | chance-constrained control  \nABSTRACT  \nThis paper proposes Scenario-Based Data-Enabled Predictive Control (Scenario-DeePC), which integrates the scenario optimization framework into Data-enabled Predictive Control (DeePC) to provide probabilistic guarantees on constraint satisfaction under uncertainty. In contrast to existing methods, the uncertainty is characterized directly from data by constructing empirical disturbance scenarios from observed prediction errors, keeping the method fully consistent with the data-driven philosophy of DeePCand free of distributional assumptions. We establish the supporting theory, including a distribution-free probabilistic guarantee on constraint satisfaction and recursive feasibility ofthe receding-horizon scheme. An adaptive extension collects scenarios online, enabling the controller to adjust to changing noise characteristics, disturbances, and operating-point-dependent model mismatches. The approach is demonstrated on a linear Boeing 747 model and a nonlinear two-tank system, showing a significant reduction in constraint violations compared to standard DeePC, while maintaining comparable tracking performance in nominal conditions and improving tracking accuracy in the nonlinear setting.  \n1  Introduction  \nData-enabled Predictive Control, first proposed in [1], provides a purely data-driven alternative to traditional Model Predictive Control (MPC) . Rather than constructing and relying on an explicit parametric model of the system dynamics, DeePC directly formulates the optimal control problem using measured inputoutput data, avoiding an intermediate modeling step and the errors it can introduce. DeePC relies on Willems fundamental lemma [2], according to which measured trajectories of a persistently excited deterministic linear time-invariant (LTI) system span the entire behavior of the system. Organizing these trajectories in Hankel matrices provides a data-based representation of admissible future evolutions conditioned on past input-output sequences. This behavioral representation replaces the classical model-based predictor and removes the need for explicit knowledge of the system equations. Real-world systems, however, are typically affected by measurement noise and may exhibit nonlinear behavior. As demonstrated in [1, 3, 4, 5, 6], these challenges  \ncan be addressed by incorporating suitable regularization terms into the DeePC optimization problem. Such regularization relaxes the exact trajectory matching conditions implied by the lemma and enhances robustness with respect to noisy data and mild nonlinearities. Nevertheless, while regularization improves robustness of the implicit data-driven identification step in the trajectory reconstruction, as reviewed in [7, Sec. 3.2], it does not explicitly address the risk of constraint violations arising from uncertainty, like measurement noise and disturbances. Therefore, approaches explicitly addressing robust constraint satisfaction, ensuring performance and safety simultaneously, remain an open research question within data-driven predictive control. A principled framework for constraint satisfaction under uncertainty is provided by the scenario approach [8, 9, 10], which replaces intractable chance-constrained fo","cbCairf93XDUzfJL","https://ap.wps.com/l/cbCairf93XDUzfJL","pdf",2485219,1,13,"English","en",105,"# Introduction\n## Data-enabled Predictive Control (DeePC)\n## Regularization and robustness limits\n## Scenario approach for constraint satisfaction under uncertainty\n# Proposed Scenario-DeePC","[{\"question\":\"What problem does Scenario-DeePC address in data-enabled predictive control?\",\"answer\":\"It targets the risk of constraint violations caused by uncertainty such as measurement noise and disturbances, which DeePC regularization mainly improves for trajectory reconstruction but not explicitly for constraint satisfaction.\"},{\"question\":\"How does Scenario-DeePC characterize uncertainty without distributional assumptions?\",\"answer\":\"It constructs empirical disturbance scenarios from observed prediction errors, deriving uncertainty directly from data rather than assuming a known disturbance distribution.\"},{\"question\":\"What guarantees and computational properties are established in the paper?\",\"answer\":\"The paper provides distribution-free probabilistic guarantees for constraint satisfaction and shows recursive feasibility for the receding-horizon scheme, while keeping the formulation computationally tractable by using sampled constraints.\"}]",1784182971,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"scenario-based-data-enabled-predictive-control-robustification-via-the-scenario-approach","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/scenario-based-data-enabled-predictive-control-robustification-via-the-scenario-approach/82793/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does Scenario-DeePC address in data-enabled predictive control?","Question",{"text":74,"@type":75},"It targets the risk of constraint violations caused by uncertainty such as measurement noise and disturbances, which DeePC regularization mainly improves for trajectory reconstruction but not explicitly for constraint satisfaction.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does Scenario-DeePC characterize uncertainty without distributional assumptions?",{"text":79,"@type":75},"It constructs empirical disturbance scenarios from observed prediction errors, deriving uncertainty directly from data rather than assuming a known disturbance distribution.",{"name":81,"@type":72,"acceptedAnswer":82},"What guarantees and computational properties are established in the paper?",{"text":83,"@type":75},"The paper provides distribution-free probabilistic guarantees for constraint satisfaction and shows recursive feasibility for the receding-horizon scheme, while keeping the formulation computationally tractable by using sampled constraints.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & 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