# In-the-Loop Simulation Experiment of Aero-Engine Fault-Tolerant Control Technology
## 1 Introduction
[20] solved the contradiction between less measured parameters and more health parameters by linearly combining health parameters. [PERSON] et al. [21] proposed the square root traceless algorithm to solve the inaccuracy of the Kalman filter caused by the reduction of model linearity. [PERSON] et al. [22] analyzed various extended Kalman filters and found that the optimal selection of a turbofan engine is the constrained extended Kalman particle filter. Fault-tolerant control technology is mainly divided into the following two parts: the passive fault-tolerant method aims to reduce the sensitivity of the system to faults, and generally adopts a robust controller; active fault tolerance generally adopts the methods of control loop switching or control rate adjustment and uses the obtained parameters to adjust the controller to avoid the harm caused by fault [23]. Fault-tolerant control was studied earlier abroad. [PERSON] et al. [14] compared the output of the engine step input with the normal output of the engine, set the threshold to judge whether there was a fault, reconstructed the analytical redundancy, and finally realized the sensor fault tolerance. [PERSON] et al. [24] applied fuzzy theory to fault tolerance for the first time. [PERSON] et al. [25] opened the self-associative neural network, that is, the pioneer of artificial intelligence applied to engine fault tolerance. [PERSON] et al. [26] solved the analytical redundancy based on the synovial observer and realized the sensor fault tolerance, and the actuator fault tolerance based on the synovial control. Finally, they were successfully applied to civil aircraft. [PERSON] et al. [27] combined an adaptive neural network and fuzzy control theory to solve the high requirements for real-time and robustness of control system data acquisition. The research on fault-tolerant control in China is also fruitful. According to the characteristics of the Riccati equation, [PERSON] et al. [28] obtained a sufficient condition and corresponding algorithm to make the sensor failure complete. [PERSON] et al. [29] adopted the method of eigenstructure configuration to make the system obtain strong anti-interference ability, that is, robustness. [PERSON], [PERSON] et al. [30] used a neural network to approach the fault and offset the fault in backstepping control to realize fault tolerance. [PERSON] et al. [31] adopted the \"fault simulation rod\" method for single- and dual-channel faults of the engine speed sensor and conducted a bench test. [PERSON] et al. [32] carried out fault-tolerant control by switching the control strategy and control mode. The sensor fault diagnosis and fault-tolerant control of the aeroengine control system usually use the method of hardware redundancy: multiple sensors are used to measure the same parameter. When one sensor fails, the fault can be diagnosed through the methods of range, change rate detection, and redundant sensor voting, and switched to the normal sensor channel to make the control system work normally [33]. However, with the increase in hardware redundancy, the weight and volume of the engine will inevitably increase and even reduce the overall performance of the system [34]. In order to avoid the above problems, the analytical redundancy is generally taken as the redundancy information, that is, according to the internal relationship between various engine variables, the measured value of a parameter can be estimated by other sensors. This estimate is the analytical redundancy (or software redundancy to distinguish it from hardware redundancy), which can provide a reference value for sensor fault diagnosis or provide the voting reference value of the third channel for sensor fault diagnosis with dual hardware redundancy, and judge whether the fault occurs according to the residual between the estimated value and the measured value. For sensors without hardware redundancy, analytical redundancy can also be used to replace the failed sensor.