In order to verify the functionality of the composite control system of the interceptor, a hardware-in-the-loop simulation system was constructed, and simulation verification was carried out. Firstly, a six-degree-of-freedom dynamic mathematical model of the interceptor. Then the interceptor composite control system was designed, including a direct force control subsystem, an aerodynamic control subsystem and a composite control strategy based on the interception process analysis. We created a tiny hardware-in-the-loop simulation system and examined its confidence by looking at the hardware model, software platform, data interaction network, and system design. Lastly, the control effects of hardware-in-the-loop simulation and full digital simulation were compared under various interception scenarios. The results confirm the accuracy and efficacy of the interceptor's composite control system and demonstrate the high degree of confidence in the hardware-in-the-loop simulation system, which can be used to validate models and algorithms.

Due to the correlation between flight and ground test data, there is a certain difference between the wind tunnel test data and the flight test data. Limited by the flight trajectory and test excitation, the flight test data distributes in a small characteristic sections of the flight trajectory. And due to the limitation of ground simulation capabilities, wind tunnel tests have certain differences between the test data and the actual flight data. In order to remedy these limitations, researches of the data fusion algorithm based on above two different sources of aerodynamic data are conducted. Firstly, through the aerodynamic parameter identification method, the six-component real aerodynamic data of the aircraft along the actual flight trajectory are obtained, and then compare the difference and consistency with the wind tunnel test data. Finally, two kinds of data fusion methods based on gradient information and Gaussian process regression were used to fuse the two sources of aerodynamic datas. The results show that both the prediction data of the two fusion models are more accurate than the single source model; if the gradient information of the high and low precision data is more consistent, the data fusion method based on the gradient information is more effective; while the data fusion method based on the Gaussian process regression could obtain the confidence interval of the fusion data, which is useful to the analysis of uncertainty.

Wind-tunnel experiments, numerical simulations, and flight tests are the three major means for aircraft aerodynamic research. However, each method has its limitation, thus it is difficult to accurately predict aerodynamic characteristics of an aircraft in its full flight envelope by a single approach. Data fusion is important for improving accuracy and enhancing forecasting capabilities. In the parameter space, the fused data could be a supplement to high-fidelity data. Meanwhile, the fused data outside the parameter space can provide a reference for the variation trend of the data. Therefore, aerodynamic data obtained by different methods need to be fused. To this end, two data fusion algorithms are proposed. One is a weighted fusion algorithm based on uncertainty, which uses the Gaussian process regression to obtain the characteristics of aerodynamic data from different sources before performing a weighted fusion. The other is an agent fusion model which is established by using the CoKriging algorithm. Aerodynamic data of an aircraft are taken for comparative analyses. Results show that the prediction accuracy of the Kriging model using single-precision data can be improved by more samples. Both fusion algorithms have significant higher accuracy than the Kriging model using only single-precision data. But the accuracy of the CoKriging algorithm is roughly one order of magnitude higher than the fusion algorithm based on uncertainty.