{"id":16651060,"url":"https://github.com/eric-bradford/sdd-gp-mpc","last_synced_at":"2025-06-16T03:09:07.019Z","repository":{"id":201970038,"uuid":"609974043","full_name":"Eric-Bradford/SDD-GP-MPC","owner":"Eric-Bradford","description":"This repository contains the source code for \"Stochastic data-driven model predictive control using Gaussian processes\" (SDD-GP-MPC).","archived":false,"fork":false,"pushed_at":"2023-04-09T09:18:23.000Z","size":19246,"stargazers_count":50,"open_issues_count":0,"forks_count":6,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-03T22:41:24.368Z","etag":null,"topics":["casadi","chemical-engineering","constraints","differential-equations","gaussian-processes","machine-learning","model-predictive-control","monte-carlo-simulation","optimization-algorithms","python3","state-space-model","stochastic-processes"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Eric-Bradford.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-03-05T19:30:20.000Z","updated_at":"2025-03-18T23:40:24.000Z","dependencies_parsed_at":"2024-05-10T22:30:53.645Z","dependency_job_id":null,"html_url":"https://github.com/Eric-Bradford/SDD-GP-MPC","commit_stats":null,"previous_names":["eric-bradford/sdd-gp-mpc"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Eric-Bradford/SDD-GP-MPC","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FSDD-GP-MPC","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FSDD-GP-MPC/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FSDD-GP-MPC/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FSDD-GP-MPC/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Eric-Bradford","download_url":"https://codeload.github.com/Eric-Bradford/SDD-GP-MPC/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eric-Bradford%2FSDD-GP-MPC/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260089796,"owners_count":22957150,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["casadi","chemical-engineering","constraints","differential-equations","gaussian-processes","machine-learning","model-predictive-control","monte-carlo-simulation","optimization-algorithms","python3","state-space-model","stochastic-processes"],"created_at":"2024-10-12T09:23:36.937Z","updated_at":"2025-06-16T03:09:06.976Z","avatar_url":"https://github.com/Eric-Bradford.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Stochastic data-driven model predictive control using Gaussian processes\n---\nThis repository contains the source code of the work in *[Bradford et al., 2020](#Bradford2020)*. In this work we proposed a new method to design a GP-based NMPC algorithm for finite horizon control problems. The method generates Monte Carlo samples of the GP offline for constraint tightening using back-offs. The tightened constraints then guarantee the satisfaction of chance constraints online. Advantages of our proposed approach over existing methods include fast online evaluation, consideration of closed-loop behaviour, and the possibility to alleviate conservativeness by considering both online learning and state dependency of the uncertainty. The algorithm is verified on a challenging semi-batch bioprocess case study. \n\nIf you found this code helpful please consider citing *[Bradford et al., 2020](#Bradford2020)*. \n\n\u003cimg src=\"https://ars.els-cdn.com/content/image/1-s2.0-S0098135419313080-fx1.jpg\" alt=\"\" height=\"250\"\u003e\n\n---\n## Highlights\n\n\u003cdiv id=\"abssec0001\"\u003e\u003cp id=\"sp0001\"\u003e\u003cdl class=\"list\"\u003e\u003cdt class=\"list-label\"\u003e•\u003c/dt\u003e\n\n\u003cdd class=\"list-description\"\u003e\u003cp id=\"p0001\"\u003eA robust data-driven \u003ca href=\"/topics/engineering/predictive-control-model\" title=\"Learn more about model predictive control from ScienceDirect's AI-generated Topic Pages\" class=\"topic-link\"\u003emodel predictive control\u003c/a\u003e algorithm is presented.\u003c/p\u003e\u003c/dd\u003e\u003cdt class=\"list-label\"\u003e•\u003c/dt\u003e\n\n\u003cdd class=\"list-description\"\u003e\u003cp id=\"p0002\"\u003eConstruction of a probabilistic state space model using Gaussian processes.\u003c/p\u003e\u003c/dd\u003e\u003cdt class=\"list-label\"\u003e•\u003c/dt\u003e\n\n\u003cdd class=\"list-description\"\u003e\u003cp id=\"p0003\"\u003eBack-offs are computed offline using closed-loop Monte Carlo simulations.\u003c/p\u003e\u003c/dd\u003e\u003cdt class=\"list-label\"\u003e•\u003c/dt\u003e\n\n\u003cdd class=\"list-description\"\u003e\u003cp id=\"p0004\"\u003eIndependence of samples allows probabilistic guarantees to be derived.\u003c/p\u003e\u003c/dd\u003e\u003cdt class=\"list-label\"\u003e•\u003c/dt\u003e\n\n\u003cdd class=\"list-description\"\u003e\u003cp id=\"p0005\"\u003eExplicit consideration of online learning and state dependency of the uncertainty.\u003c/p\u003e\u003c/dd\u003e\u003c/dl\u003e\u003c/p\u003e\u003c/div\u003e\n\n---\n## Getting started\nCreate a new environment in conda using the *environment.yml* file:\n\n``` \nconda env create --file environment.yml \n```\nThen you should be able to run the simulation file *[GP_NMPC_batch_simulation.py](GP_NMPC_batch_simulation.py)*. To adjust the problem, simply amend the problem definition given in *[Problem_definition.py](Problem_definition.py)*. \n\n---\n## Reference\nBradford, E., Imsland, L., Zhang, D., del Rio-Chanona, E.A., 2020. [Stochastic data-driven model predictive control using Gaussian processes](https://doi.org/10.1016/j.compchemeng.2020.106844). Computers \u0026 Chemical Engineering 139, 106844.\n\u003ca name=\"Bradford2020\"\u003e\n\u003c/a\u003e\n\n---\n## Acknowledgements\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No 675215.\n\n---\n## Legal information\nThis project is licensed under the MIT license – see *[LICENSE.md](LICENSE)* in the repository for details.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feric-bradford%2Fsdd-gp-mpc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feric-bradford%2Fsdd-gp-mpc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feric-bradford%2Fsdd-gp-mpc/lists"}