{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stochastic-nonlinear-model-predictive-control","title":"Stochastic Nonlinear Model Predictive Control with State Estimation by Incorporation of the Unscented Kalman Filter","arxiv_id":"1709.01201","date":"2017-09-05","proceeding":null,"authors":["Eric Bradford","Lars Imsland"],"abstract":"Nonlinear model predictive control has become a popular approach to deal with highly nonlinear and unsteady state systems, the performance of which can however deteriorate due to unaccounted uncertainties. Model predictive control is commonly used with states from a state estimator in place of the exact states without consideration of the error. In this paper an approach is proposed by incorporating the unscented Kalman filter into the NMPC problem, which propagates uncertainty introduced from both the state estimate and additive noise from disturbances forward in time. The feasibility is maintained through probabilistic constraints based on the Gaussian approximations of the state distributions. The concept of robust horizon is introduced to limit the open loop covariances, which otherwise grow too large and lead to conservativeness and infeasibility of the MPC problem. The effectiveness of the approach was tested on a challenging semi batch reactor case study with an economic objective.","url_abs":"https://arxiv.org/abs/1709.01201v1","url_pdf":"https://arxiv.org/pdf/1709.01201v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"stochastic-nonlinear-model-predictive-control","repo_url":"https://github.com/Eric-Bradford/UKF-SNMPC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}