# In-the-Loop Simulation Experiment of Aero-Engine Fault-Tolerant Control Technology
## 3 Results
### Hardware in the Loop Simulation
#### 3.2.3 The Results of Hardware In-the-Loop Simulation  
Firstly, the analytical redundancy is verified. At the design point, the performance degradation of four rotating parts is injected at the same time at 10 s, the sensor is set with  
Figure 6: Dual-core processor module based on NXP QorIQ P2020.  
Figure 7: Schematic diagram of fault tolerant control hardware in-the-loop simulation of turbofan engine.  
5% bias fault at 85 s (limited to space, this experiment selects high- and low-speed faults as an example), the fault recovers after 20 s, and stops running at 120 s.  
As shown in Figure 8, after the injection performance is degraded, the adaptive model tracks well, and after the high- and low-pressure speed fault is injected, the fault sensor is accurately isolated. Therefore, the adaptive model is not affected and plays the role of fault-tolerant control.  
As shown in Table 7, the sensor real value of the real engine and the sensor estimated value of the adaptive model under the stable state of 120 s are selected, and the error between them is calculated. The error is no more than 0.35%. It is verified that the estimation accuracy is high. The reason for the error may be that theoretically, the state-space model and Kalman gain matrix K correspond to the steady-state point of the engine, but the residual in this paper is directly obtained from the nonlinear model. Compared with the linear model at different working points, the error is small, so only one constant Kalman gain matrix can be selected for simplification. However, a small error does not mean that the error does not exist, so it may be that there is no 100% matching of the adaptive model.  
The purpose of this experiment is to verify the fault-tolerant control effect of the controller using analytic redundancy for sensor faults on the embedded platform.  
Figure 9 shows the simulation results of fault-tolerant control using the analytical redundancy of the adaptive model as the control feedback quantity when the injection performance degrades and the sensor fails at 0 height and 0 Mach.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Nh** & **NI** \\ \hline Sensor true value of & & \\ real engine/\% & 97.00000 & 98.01408 \\ Sensor measurements of & & \\ real engine/\% & 97.05500 & 97.96907 \\ Sensor estimation of & & \\ adaptive model/\% & 96.73110 & 97.67080 \\ Error/\% & 0.00277 & 0.00350 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of analytical redundancy estimation of high- and low-pressure speed fault at design point.  
Figure 8: Analytical redundancy verification of high- and low-pressure rotating speed fault at design point. (**a**) Analytical redundancy verification of high-pressure rotating speed fault at design point. (**b**) Analytical redundancy verification of low-pressure rotating speed fault at design point.  
The simulation process is as follows: the injection performance degraded at 10 s (i.e., the health parameters changed), the sensor fault was set at the 80 s (forward bias 5%), and the fault was recovered at 120 s. The throttle instructions were randomly changed throughout the whole process.  
It can be seen from the experimental results that the adaptive model can accurately estimate the component performance parameters after a period of time, even if the throttle command changes irregularly after the injection performance degradation. The external performance is that it tracks the real engine. Then, after the failure of the Nh speed sensor (forward bias 5%), the control system uses the estimated value of the adaptive model as the analytic redundancy feedback to participate in the closed-loop control, so as to avoid the serious consequences caused by using the wrong fault value as feedback, and ensure the safety control and normal performance of the engine.