To improve the control performance in the transient state of a gas turbine engine, a new method based on variable replacement method (VRM) and particle swarm optimization (PSO) algorithm is proposed. Above all, an acceleration schedule under the constraints of fuel air ratio (FAR), high pressure turbine inlet temperature (T₄) and high pressure compressor surge margin (SM) could be obtained. Then PSO is employed to optimize the acceleration controller. At the same time, the deceleration controller is designed in consideration of minimum fuel ratio unit (W_f/P_s3) limiter. At last, a simulation is carried out to verify the performance of the proposed method and the results manifest that the optimized controller can track the acceleration command quickly and accurately, while accomplish the requirement of minimum fuel ratio unit (0.005) in deceleration.

A novel wireless power transfer approach for the rotary parts telemetry of a gas turbine engine is proposed. The advantages of a wireless power transfer (WPT) system in the power supply for the rotary parts telemetry of a gas turbine engine are introduced. By simplifying the circuit of the inductively-coupled WPT system and developing its equivalent circuit model, the mathematical expressions of transfer efficiency and transfer power of the system are derived. A mutual inductance model between receiving and transmitting coils of the WPT system is presented and studied. According to this model, the mutual inductance between the receiving and the transmitting coils can be calculated at different axial distances. Then, the transfer efficiency and transfer power can be calculated as well. Based on the test data, the relationship of the different distances between the two coils, the transfer efficiency, and transfer power is derived. The proper positions where the receiving and transmitting coils are installed in a gas turbine engine are determined under conditions of satisfying the transfer efficiency and transfer power that the telemetry system required.

A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine’s dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

A novel control scheme for a thrust vector system with an electromechanical actuator is proposed. The proposed method merges the idea exploited in sliding mode control (SMC) with proportion- integration-differentiation (PID) control, which brings advantages in terms of strong robustness and good position tracking performance, while the chattering of the control signal produced by the SMC is small. A model for a typical thrust vector system with an electromechanical actuator is established and analyzed. To evaluate the performance of the proposed approach, the traditional PID controller mixed with bangbang control and the proposed compound control law is applied to the two-channel model of the thrust vector system with an electromechanical actuator. Various simulation examples are provided to show that this compound control law has stronger robustness to the model parameter uncertainty and better position tracking performance. In addition, the chattering is slight.

The authors wish to make the following corrections to the published paper