Multivariable Constrained Predictive Control and its Application to a Commercial Turbofan Engine

Abstract

Aeroengine controller is a crucial and complex component aimed at pursing optimal performance while satisfying all kinds of physical and operational constrains. A novel control technique, multivariate constrained predictive control based on linear state space model, is applied to a commercial turbofan engine. To be specific, according to the control requirements of turbofan engine, its outputs are classified into two types, controlled or constrained; they are then incorporated into the cost function and the inequality constraint condition of baseline MPC algorithm respectively. Further, since key parameters of MPC play an important role in obtaining optimal performance, their selection is studied. It is shown that control performance using improved MPC is better than that of PID controller for a big disturbance while maintaining within prescribed constrains.