Event-Triggered Model Predictive Control for Aero-Engine Transient-State Tracking Problems

Abstract

Transient-state control is one of the most important parts in aero-engine design. It determines the performance of an aero-engine or even the maneuverability of an aircraft. An optimized transient-state process can stimulate the potential performance of an aero-engine. Thus, model predictive control (MPC) is widely applied in transient-state tracking control with complex control constraints. However, the enormous computation pressure restricts its implementation in aero-engine transient-state tracking control. This paper aims to improve the efficiency of MPC and investigates the event-triggered model predictive control (EMPC) for aero-engine transient-state tracking problems. A new event-triggered mechanism that can exclude the Zeno behavior without an artificial interevent time is designed based on linear parameter-varying (LPV) aero-engine models. The feasibility and stability of the designed EMPC are investigated, and a numerical simulation example is presented to show the effectiveness of the designed method. The simulation result shows that our proposed method can reduce the computation load and time consumption significantly compared with MPC.