Comparison of model predictive controller and optimized min-max algorithm for turbofan engine fuel control

Abstract

Min-max selector structure is traditionally used as the industrial control architecture of commercial turbofan engines. However, recent studies indicate that this structure with linear compensators suffers from lack of safety guarantee in fast demands. On the other hand, model predictive control (MPC) technique, which incorporates input/output constraints in its optimization process, has the potential to fulfill the control requirements of an aircraft engine. In this paper, a practical approach is performed for design and optimization of the turbofan engine controller through a comparative study where all control modes and requirements have been taken into account simultaneously. For this purpose, a thermodynamic nonlinear model is firstly developed for the turbofan engine. The linear regulators of minmax structure are then optimized via genetic algorithm (GA). The MPC technique is formulated based on the proper discrete-time linearized state-space models at desired operating points with real-time optimization, in which the MPC tuning horizons are obtained through GA optimization procedure. The both controllers are implemented on appropriate hardware taking the real-time aspects into account. Finally, a hardware in the loop (HIL) platform is developed for the turbofan engine electronic control unit (ECU) testing. The software and HIL simulation results confirm that MPC improves the response time of the system in comparison with min-max algorithm and guarantees the engine limit protection. This study demonstrates competitive advantages of MPC in terms of limit protection assurance and fast response, despite more computational burden.