An Integrated Optimization and Orchestration Toolchain for Adaptive Optimal Control in Modelica Simulations

Abstract

This paper introduces a novel Python-based toolchain, "OptiOrch", designed to enhance optimal control in Modelica-based simulations by integrating an optimization framework and an orchestration workflow. OptiOrch leverages the "MOO4Modelica" optimization framework, which supports both single- and multi-objective parameter optimization, and incorporates the "ModelicaOrch" orchestration workflow to dynamically adapt models based on real-time input data and goals. The toolchain features a user-friendly interface, feature model transformation, parallel computing, and automated workflow coordination, making it a powerful and generalized solution for various applications. Practical examples and a case study demonstrate how this toolchain can be effectively applied to Modelica systems for optimal control.