Nonlinear model predictive control in modelica using FMI and optimization library

In this work-in-progress paper, a currently ongoing development of a generic tool for nonlinear model predictive control is presented. By using an extended interface of FMI 2.0, it is possible to simulate a model that acts as prediction model while the actual system is simulated simultaneously. A trajectory optimization that uses the prediction model provides optimized input control values for the actual system at every sample time. The current work is based on the Optimization library for Dymola and an extended version of FMI 2.0 Co-Simulation. The structure of this approach is explained in detail as well as possible settings and limitations. An example shows the practicability and an outlook for further development is given.