Nonlinear model predictive control of an aircraft gas turbine engine

Abstract

The feasibility of constrained nonlinear model predictive control (NMPC) with state estimation is investigated and applied to a high-fidelity turbojet aircraft engine model. Strong nonlinearities are present in turbojet aircraft engines due to the large range of operating conditions and power levels experienced during a typical mission. Also, turbine operation is restricted due to mechanical, aerodynamic, thermal, and flow limitations. NMPC is selected because it can explicitly handle the nonlinearities, and both input and state constraints of many variables in a single control formulation. Due to the computational requirements of NMPC and the fast dynamics of aircraft engines, a Simplified Real Time Model (SRTM) is created that captures all of the relevant dynamics while executing quickly. An Extended Kalman Filter (EKF) is applied to estimate the states in the presence of noise and limited sensor data. This output feedback controller is tested on a high-fidelity model of a military aircraft engine, showing that NMPC based on the simplified model has the potential to achieve better performance than the production controller.