Gas Turbine Engine Control and Management using Adaptive Model Predictive Control

Safe operation of Gas Turbine Engines (GTEs) is one of the paramount missions in the GTE industry. Ensuring reliable operation and control performance is a challenging task that involves optimizing engine performance across various operating points. The Model Predictive Control (MPC) technique is a powerful approach that provides an optimization framework to meet the management and control requirements of GTEs during their operation. This work presents a systematic control framework to efficiently control a GTE. The proposed control algorithm employs an adaptive MPC methodology that ensures both safe and efficient engine operation. The MPC structure is adapted across varying operating points, and its parameters are optimized to provide control inputs that ensure best engine performance during both transient and steadystate engine operation. To enhance control reliability, the MPC is systematically coupled with min-max limiters to maintain critical variables within allowable bounds, ultimately to prevent engine surge, flame-out, and exceeding unsafe variable limits. Through this approach, the aim is to achieve a superior level of safety and operability for GTE control and management. Simulation results on a generic commercial turbofan engine with a 40,000-pound thrust show that the proposed algorithm outperforms a gain-scheduled PI controller with min-max limiters, and demonstrate enhanced performance in engine management and control.