A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature.

In order to improve the real-time performance of performance seeking control (PSC), a neural network-propulsion system matrix(NN-PSM) on-board model is proposed and applied to PSC. First, based on NN-PSM, a large-envelope, multi-variable on-board adaptive model is established. The PSM is extracted through a small deviation linearization method. The neural network is used to map the relationship between the flight conditions, engine control parameters, and the engine performance parameters, and designed a Kalman filter to estimate engine health parameters in real-time. Then, four PSC mode of maximum thrust, minimum fuel consumption, minimum high-pressure turbine inlet temperature, and minimum infrared radiation intensity are designed using LP optimization algorithm as optimize algorithm. Finally, the simulation results show that NN-PSM has much higher precision than Compact Propulsion System Model (CPSM). The PSC simulations show that compared with the PSC based on the conventional CPSM, the proposed method has much better real-time performance and get better engine performance, such as more thrust, less specific fuel consumption, and less turbine inlet temperature.

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.