As per few investigation in installed performance for variable cycle engines, an analysis scheme is proposed on the basis of integrating variable cycle engine and supersonic inlet system model. An integrated mathematical model, containing the inlet and the variable cycle engine is built, realizing the simulation of influences on the installation performance by varying geometry components. The impact on engine performance of variable geometric regulation was analyzed and concluded respectively. The experimental results show that the overflow resistance of the variable cycle engine with variable geometry optimization is reduced at subsonic cruise stage, and the installed fuel consumption is reduced, which significantly improves the installation performance.

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.

The process of rotor speed variation under tiltrotor cruise state has been studied, and the integrated variable speed control method of tiltrotor based on two-speed gearbox is proposed. Firstly, a nonlinear model predictive controller (NMPC) based on state variable model of the component-level model of the turboshaft engine is designed. Then based on the integrated engine model, the two-speed dual path tiltrotor driveline comprehensive simulation model was developed by utilizing gear kinematics theory, blade element analysis and the theory of classical mechanics. Finally, both a Parallel Shift Control (PSC) strategy and a Sequential Shift Control (SSC) strategy in tiltrotor cruise state were analyzed and compared with conventional PID controller. It is shown that the rotors’ speed can synchronously vary by 50 percent under the PSC strategy in tiltrotor cruise state. Meanwhile, disengaging the engine in turn by freewheel clutches can reduce the rotors’ speed from 190 rpm to 102.5 rpm along the specified path under the SSC strategy. The overshoot and droop amount of the power turbine speed can be reduced to less than 1.5 % with the steady error no more than 0.2 % through NMPC, which realizes the fast response control of the turboshaft engine.

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.