The electro-hydrostatic actuator (EHA) has a high energy density and a compact structure so it is widely used in all/more electric aircraft. To gain energy-saving capability, the hydraulic lock can be applied in the EHA system, which can overcome the change of load after the position control is complete, reducing the motor’s need to work continuously. Due to the use of a hydraulic lock in the EHA system, there is a more significant position output delay compared with the EHA system. The delay would lead to a dynamic response error. Therefore, an electro-hydrostatic actuator with a bidirectional hydraulic lock (BHL-EHA) model is established that can reflect the position output delay and it can be used to analyze its influence factors. According to the analysis, the factors affecting the delay are pump speed, external load, hydraulic lock cracking pressure, friction load, pump displacement, fluid volume of the system, and effective bulk modulus; thus, an equation that can be used to calculate the delay is proposed. At the same time, the experimental system of the BHL-EHA system is established, and the parameters of the experimental facility are completely consistent with the simulation model. Finally, a delay compensation control method is proposed, and the control time parameter $t_0$ is calculated based on the equation proposed above. The results of the simulation and the experiment show that the method can compensate for the delay caused by the hydraulic lock. Combined with a feedforward control, the method can dismiss the dynamic response error of 0.6 Hz sinusoidal tracking characteristics.

The main data source for the verification of surge detection methods still rely on test rigs of the compressor or the whole engine, which makes the development of models of the whole engine surge process an urgent need to replace the high-cost and high-risk surge test. In this paper, a novel real-time surge model based on the surge mechanism is proposed. Firstly, the turboshaft engine component level model (CLM) and the classic surge dynamic model, Moore-Greitzer (MG) model is established. Then the stability of the MG model is analyzed and the compressor characteristics in the classical MG model are extended to establish the extended MG model. Finally, this paper considers the coupling relationship of the compressor’s rotor speed, mass flow and pressure between CLM and the extended MG model to establish the real-time model of the turboshaft engine with surge process. The simulation results show that this model can realize the whole surge process of the turboshaft engine under multiple operating states. The change characteristics of the rotor speed, compressor outlet pressure, mass flow, exhaust gas temperature and other parameters are consistent with the test data, which means that the model proposed can be further applied to the research of surge detection and anti-surge control.

The existing aeroengine instability precursor detection methods can be summarized as applying advanced signal processing technologies to various signals from the compressor test rig rather than the whole engine. Besides, these methods seriously depend on the artificial designed feature and threshold and also ignore the limit on the sensors onboard. Thus, with the help of the powerful feature extraction ability of the deep neural network, a real-time surge prediction method based on the multi-branch feature fusion neural network (MBFFNN) is proposed. First, the dataset can be obtained by using overlapping slices to divide surge test data into a sample sequence and using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to label each sample precisely. Second, for each sample, the time-domain statistical parameters are calculated and the recurrence plot is obtained by using phase space reconstruction. Finally, the MBFFNN with mixed data type input is designed, and its performance is evaluated by the generated dataset. The experimental results show that compared with multilayer perceptron (MLP), long short-term memory (LSTM), and deep residual network (DRN), MBFFNN has the best performance on two datasets for different surge tests, which demonstrates that the proposed method for surge prediction can accurately judge the state of the aeroengine, identify the instability precursor before the surge, and give an early warning in advance.