## 5 Hardware-in-the-Loop Simulation and Verification  
The hardware-in-the-loop (HIL) test platform is shown in Figures 23 and 24, which consists of an engine simulator, a distributed controller and a monitoring computer. The engine simulator includes NI myRIO and interface simulator. NI myRIO is responsible for the real-time operation of the engine model and simplified actuator model, and the interface simulator is responsible for simulating the signals of various sensors of the engine and collecting the current signals of actuator valves. The distributed controller comprises several intelligent nodes, including high and low spool speed acquisition node, pressure acquisition node, fuel flow control node, nozzle area control node, core control node and backup. Each intelligent node is an embedded system based on the ZYNQ development board and communicates with each other via the TTP/C bus. In addition to the tasks specified by TTP/C communication, each intelligent node also needs to complete the duties prescribed by the control system, which is conducted by two CPUs in the logical processing unit and the program processing unit in ZYNQ. The nodes are programmed and developed by Vivado's integrated development environment and SDK software development suite. The monitoring computer is responsible for sending control instructions and monitoring the engine state, and is produced by LabVIEW. In terms of time schedule, the simulation  
Figure 22: Changing curve of control quantity in acceleration FTC process. (**a**) Fuel flow. (**b**) Nozzle area. step size of the engine simulator is 20 ms. In the distributed control system, the step size of the data acquisition node and the core control node is also 20 ms, while the step size of the actuator control node is 5 ms. Data transmission of each node on the TTP/C bus has a specified time sequence and is scheduled by a pre-planned Message Description List (MEDL). The low spool speed, the high spool speed, the total pressure and the total temperature at the outlet of the high-pressure compressor are also selected for fault diagnosis and design. FTC results are shown in Figures 25-28. At 3 s, the acceleration control starts. At this time, the speed of the engine and the speed of the on-board model is both good for tracking instructions. At about 3.8 s, a 5% bias occurs to the low spool speed sensor, as shown by the red dash in Figure 26. The controller rapidly increases the fuel flow control instruction, as shown in the solid black line in Figure 28. However, different from the result in 4.3, since the change rate of the actuator has reached the limit at this time, the actual fuel volume does not increase dramatically but maintains the maximum rate of increase. As shown in Figure 25, the low spool speed bias is quickly diagnosed by the Kalman filter, and the diagnosis effect is noticeable. At about 4 s, the reconstructed signal of the airborne model is switched for FTC and the fuel flow control instruction falls back. At 6.8 s, the reconstructed low spool speed signal keeps up with the control instruction and tends to a steady state, and the engine accelerates successfully. The fault diagnosis and signal reconstruction can be  
Figure 24: Schematic diagram of distributed control system structure. Figure 23: Hardware-in-the-loop simulation platform. (1) Interface simulator (2) Distributed controller (3) NI myRIO (4) Monitoring computer. done within 0.2 s, and the whole FTC process can be carried out in less than 3 s. Parameters before and after the acceleration process are shown in Table 1. In general, the proposed scheme has good fault diagnosis ability and fault tolerance ability. The acceleration control under sensor fault is successfully realized, and the acceleration process does not cause a big change of engine state. Therefore, the validity of the FTC scheme proposed in this paper and its compatibility with the real-time embedded platform has been proved. Figure 27: Changing curve of CDFS surge margin and turbine front temperature in HIL test.