In the industrial automated production line, how to use the existing low-cost pneumatic equipment to obtain the best positioning effect has become a significant engineering problem. In this paper, a differential switching method is proposed in a pneumatic servo system consisting of four low-cost on–off valves for more precise control and lower prices. All valves are simultaneously open at the initial stage of each control period, and the differential closing time of the desired valves is calculated through the theoretical models. A sliding mode controller is applied with the proposed method, and the system stability is proven. The real-time control setup including three software layers is proposed to implement the algorithm. Several experiments are performed on a real-time embedded controller. Average 0.83% overshoot and 0.18 mm steady-state error are observed in the step response experiment. The highest frequency of sine wave that can be tracked is 1 Hz, and the average error is 1.68 mm. The maximum steady-state error is about 0.5 mm in the step response under payloads of 5.25 kg. All the simulation and experimental results prove the effectiveness of the control method.

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 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.

Today, the more electric aircraft (MEA) concept is gaining tremendous popularity. As a key component of the MEA, a more electric auxiliary power unit (MEAPU) integrated model with high accuracy and real-time performance is essential when conducting cooperative control and hardware-in-the-loop (HIL) test research. This paper proposes a novel MEAPU integrated model consisting of a MEAPU component-level-model (CLM) and a starter-generator (SG) model. Firstly, a MEAPU CLM was built and a continuous scaling method for the component characteristic map in the CLM is proposed to improve the model’s accuracy. Then, a double winding induction starter-generator (DWISG) model based on the electromagnetic theory, which is quite time consuming, was simplified using the pulse width modulation (PWM) rectifier linearization method. Finally, considering the coupling relationship between the MEAPU CLM and DWISG, an accurate real-time MEAPU integrated model was built and its simulation results were analyzed. Compared with the test results, the error of the proposed model was less than 0.5%; meanwhile its single-step simulation time was less than 20 ms, which can meet the demands of cooperative control and HIL test research. Furthermore, the continuous scaling method and PWM rectifier linearization method were found to be effective for modeling other MEAPUs and more electric engines (MEE).