Model Adaptation and Nonlinear Model Predictive Control of an Aircraft Engine

Abstract

The performance improvement of constrained nonlinear model predictive control (NMPC) with state and parameter estimation over traditional control architectures is investigated and applied to a model turbofan aircraft engine. Strong nonlinearities are present in turbofan 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. Current control methodologies rely strictly on a priori information; therefore they fail to utilize current engine state or health information for reducing conservatism and improving engine performance. NMPC is selected because it depends on a model that can be adapted to the current engine conditions, it can explicitly handle the nonlinearities, both input and output constraints of many variables, and determine the optimal control that will meet the requirements for any engine condition all in a single control formulation. A physics based component level model is developed as the heart of the architecture. The state or health of the engine is determined using a joint state and parameter estimator utilizing extended Kalman filter (EKF) techniques. With the necessary engine information in hand, a constrained NMPC is used to determine the optimal actuator commands. Results regarding steady state performance improvements are presented.