Results on the feasibility and benefits of model based predictive control applied to a gas turbine are presented. For a laboratory gas turbine installation, the required dynamic simulation model and the real-time (nonlinear) Model Predictive Control (MPC) implementation are discussed. Results on both model validation and control performance are presented. We applied a nonlinear MPC configuration to control the laboratory gas turbine installation and succeeded in a real-time implementation. Although the available computation time for prediction and optimization of the model limits the sample time, the advantages of MPC, i.e. constraint handling, and anticipation to future (set-point) changes are fully reached, and the control performance is good. Special attention is paid to the performance of the applied filter that compensates for inevitable mismatches between model and process measurements. In general, the opportunities of model based control of turbomachinery are promising.
Automatic Code Generation Tool for Nonlinear Model Predictive Control with Jupyter
