March 20, 2026 Software Open Access

Lopes-Júnior, Ênio; Reinecke, Sebastian Felix

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{
  "codeRepository": "https://github.com/hzdr/neural-mpcx/tree/v1.0.0", 
  "identifier": "https://doi.org/10.14278/rodare.4568", 
  "@context": "https://schema.org/", 
  "@id": "https://doi.org/10.14278/rodare.4568", 
  "url": "https://rodare.hzdr.de/record/4568", 
  "version": "v1.0.0", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Helmholtz-Zentrum Dresden-Rossendorf (HZDR)", 
      "name": "Lopes-J\u00fanior, \u00canio", 
      "@id": "https://orcid.org/0000-0002-7604-3205"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Helmholtz-Zentrum Dresden-Rossendorf (HZDR)", 
      "name": "Reinecke, Sebastian Felix", 
      "@id": "https://orcid.org/0000-0003-2705-0692"
    }
  ], 
  "description": "<p>NeuralMPCX is a Python library for building and deploying Model Predictive Controllers with classic and neural dynamical models. You write constrained MPC with RNN/LSTM models in a CasADi/IPOPT workflow. The library handles CasADi RNN integration, warm-starting, constraint management, real-time feasibility, and both LTI state-space and neural dynamics in one framework. You can run neural and classical MPC controllers side by side.</p>", 
  "license": "https://opensource.org/licenses/Apache-2.0", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-43156"
  ], 
  "datePublished": "2026-03-20", 
  "@type": "SoftwareSourceCode", 
  "name": "NeuralMPCX: A Model Predictive Control library that supports classic MPC and neural MPC with CasADi"
}