## 4 Simulation and Analysis
### Simulation Settings  
The algorithm parameters, operating environment parameters, and initial model and motion state parameters are given as follows. Parameters of the algorithm are set as following: \(h=0.05\)s, \(D_{\text{min}}=18\)km, \(\delta_{y0}=\delta_{z0}=0^{\circ}\), \(\dot{\delta}_{y0}=\dot{\delta}_{z0}=0^{\circ}/\)s, \(\delta_{\text{max}}\)=\(\pi/180\), \(G=4\). The same parameters are set for the adaptive fuzzy guidance controller in both horizontal and vertical directions, i.e., \(m_{1}=m_{2}=6\), \(k_{1}=k_{2}=1\), \(\gamma=9\), \(\mathbf{Q}=\left[ \begin{array}{cc}10&0\\ 0&10\end{array} \right]\). Both \(\mathbf{u}_{u}(t_{0})\), \(\mathbf{\theta}_{y}\) and \(\mathbf{\theta}_{z}\) are initialized to all-zero matrices. The fuzzy membership functions of \(\delta_{y}\), \(\delta_{z}\), \(\dot{\delta}_{y}\) and \(\dot{\delta}_{z}\) are set as follows: \(\mu_{N3}(x)=\exp(-((x+10)/3)^{2})\), \(\mu_{N2}(x)=\exp(-((x+6)/3)^{2})\), \(\mu_{N1}(x)=\exp(-((x+2)/3)^{2})\), \(\mu_{P1}(x)=\exp(-((x-2)/3)^{2})\), \(\mu_{P2}(x)=\exp(-((x-6)/3)^{2})\), \(\mu_{P3}(x)=\exp(-((x-10)/3)^{2})\). The initial motion states of the UCAV and the target are set as shown in Table 1.  
Other model parameters are set as \(n_{\text{max}}=9\), \(R=20\) km, \(v_{m}=600\) m/s. Besides, as comparison algorithms, in the classical discrete PID control method [28], based on empirical parameter adjustment, the control parameters in the horizontal and vertical directions are set as \(k_{p}=20\), \(k_{i}=5\), \(k_{d}=1\), where the system control errors are \(\delta_{y}\) and \(\delta_{z}\). The parameter settings in the AFC method are the same as those in the method proposed in this study. The AFC method does not perform the MPC framework and only calculates the control vector once in each time step. Furthermore, the motion states of the UCAV and the target are all updated through the fourth-order Runge-Kutta method. To further quantitatively validate the effectiveness of the proposed method, the mean aiming error angle \(I_{e}\), the duration of high-precision aiming \(I_{d}\), and the mean actual control overload \(I_{c}\) are designed as evaluation indexes to perform quantitative comparison and analysis on the above three methods. \(I_{e}\) and \(I_{c}\) can be calculated, respectively, by  
\[I_{e}=\frac{1}{t_{f}-t_{0}}\int_{t_{0}}^{t_{f}}\sqrt{\delta_{y}^{2}(t){+} \delta_{z}^{2}(t)}\;\;dt \tag{36}\]  
\[I_{c}=\frac{1}{t_{f}-t_{0}}\int_{t_{0}}^{t_{f}}\sqrt{It can be seen from Figure 8 that the three methods all generate correct attack guidance trajectories for the UCAV. In Figure 9, the aiming error angles of the three methods in both horizontal and vertical directions can reach to zero quickly and remain stable. Since the target moves in a straight line with uniform velocity, the three methods are easy to form a stable aiming state. Besides, combined with Figure 10, it can be further seen that after the initial maneuvering and aiming, the UCAV is in the attack guidance state of approximate linear trajectory all the time, i.e., \(n_{yc}=1\) and \(n_{zc}=0\). On the whole, in this simulation experiment, there is little difference between the three methods in the attack guidance effect. Figure 8: Attack guidance trajectories for the horizontal uniform linear motion of the target. Figure 10: The actual control overloads of the UCAV during the process of attack guidance. Figure 9: The aiming error angles of the UCAV during the process of attack guidance. Comparisons of quantitative evaluation indexes of the three methods in simulation experiment 1 are shown in Table 2.  
It can be seen from the data in Table 2 that the index of the proposed method is slightly better than the AFC and the PID method successively. Moreover, the proposed method also provides a slightly longer duration of high-precision aiming with slightly less control overload. However, since the target does not maneuver, this superiority is not obvious.