The simulation of an aero-engine control system involves numerous disciplines due to its complex functions and architecture, which generally consist of mechanical, hydraulic and electrical, and electronic systems. For each discipline, the modeling and simulation are usually dependent on different commercial software and tools, which makes the simulation, integration, and verification of system-level models very difficult. To meet this challenge, a multi-domain co-simulation method based on the Functional Mock-up Interface (FMI) standard is proposed to integrate models developed by different software or tools. The simulation and testing results demonstrate that multi-disciplinary model integration and cross-platform simulation based on the FMI standard can be realized for an aero-engine control system, which lays a foundation for high-fidelity control system design, simulation, integration, and testing.

The on-board dynamic model is the basis of numerous advanced control technologies for modern aero-engine, and its accuracy and real-time performance are two crucial indicators. Since the simulation accuracy declines with the deviation from the ground point of the conventional compact propulsion system dynamic model (CPSDM), an improved CPSDM (ICPSDM) based on deep neural network is proposed in this paper. First, the K-means algorithm is utilized to get the sampling point, and the engine simulation data is collected in the cluster centers. Then, the batch normalize deep neural network (BN-DNN) is applied to build the ICPSDM, which can shorten the training time tremendously. The simulation results show that, in the same simulation environment, the accuracy of turbine inlet temperature T ₄ , fan surge margin SMc, thrust F, and specific fuel consumption SFC calculated by ICPSDM are 11.04, 3.17, 1.89, and 1.98 times of CPSDM, respectively, at off-design point, and the real-time performance of ICPSDM is more than 30 times faster than the component-level model.

Under supersonic acceleration flight, the traditional passive control method brings about acceleration performance deterioration, due to the bad matching between inlet and engine. Accordingly, a real-time optimization control method of the integrated propulsion system is proposed. In the article, the effect of a supersonic inlet on propulsion system performance is analyzed, and an onboard model of the integrated propulsion system is built to calculate the installed parameters. An improved trust region algorithm is adopted to optimize the inlet ramp angle in real time. Besides, digital simulation and hardware-in-the-loop simulation are carried out. The digital simulation result shows that compared with conventional inlet passive control, the acceleration time of the propulsion system is reduced by 8.59%. The result of the hardware-in-the-loop simulation indicates that the proposed method has high real-time performance simultaneously, which proves the engineering application value.

The performance seeking control (PSC) method is aimed at improving the performance of aircraft engine without changing hardware devices. However, the complexity of the system makes it difficult to achieve its application in engineering. Thus, the key issues in PSC of turbofan engine are studied based on global sensitivity analysis, and the improvements are made to the optimization system according to the analysis. Firstly, the principle of PSC is analyzed based on traditional frameworks. And an improved linear programming method based on task scheduling is proposed to reduce the time consumption within a single closed-loop simulation. The impact of the estimation model accuracy on optimization results is studied adopting a sensitivity analysis method based on Monte Carlo sampling to address the optimization lose efficacy. Finally, the hardware-in-the-loop simulation is carried out utilizing model-based design method in a certain type of turbofan engine electronic controller.