## 3 Controller Design
### Switch Control Strategy Based on the Interval Type-2 Fuzzy Logic System  
A large number of simulation studies show that LESO has the characteristics of convenient parameter tuning, simple theoretical analysis, and strong anti-interference ability. NLESO has the advantages of high parameter efficiency, fast response speed, and high control accuracy, but it lacks the ability to estimate large disturbances and oscillations can be generated easily. The type-2 fuzzy logic system can increase the degree of freedom for the membership function [23]. It is easier to achieve smooth switching between LADRC and nonlinear NLADRC. According to the characteristics of NMVs, when NMVs ascend, the wingspan and wing area are increased by extending the winglet so as to obtain higher aspect ratio, lift drag ratio, and increase the lift. When NMVs cruise, the force area is reduced by retracting the winglet, thus reducing drag and improving the flight efficiency. However, the retraction change of the winglet will directly lead to the change of the wing area and the average aerodynamic chord length, thus changing the aerodynamic parameters, aerodynamic forces, and aerodynamic moments of the NMV. Therefore, considering mismatched uncertainties, LADRC and NLADRC are used for ascent modality and cruise modality, respectively, in order for the NMV to achieve stable flight under each modality, and the switching control strategy based on the interval type-2 fuzzy logic system is adopted to realize stable flight control during multimode switching. In the switching process, the system state tracking error \(|e_{V}|=\left|V_{1}-V_{ref}\right|\)\(|e_{h}|=\left|h_{1}-h_{ref}\right|\) and disturbance observation error \(|e_{gi}|(i=V,h)\) are set as the inputs of the fuzzy controller. The weights of LADRC and NLADRC are set as outputs of the fuzzy controller. Then, fuzzy rules can be described as follows:  
Velocity channel subsystem  
Altitude channel subsystem  
In this paper, the primary membership function is the uncertain mean value Gaussian Function (30), and the longitudinal amplitude interval is [0, 1] for the secondary member ship function. Figure 3 shows the three-dimensional graph of the Gaussian interval type-2 membership function. \[ \begin{split}\mu_{i}^{j}(x_{i})=\exp\left[-\frac{1}{2}\left(\frac{x_ {i}-\delta_{i}^{j}}{\sigma_{i}^{j}}\right)^{2}\right],\,\delta_{i}^{j}\in\left[ \delta_{i_{1}}^{j},\delta_{i_{2}}^{j}\right]\end{split}  \tag{30}\]  
In the switching process, the state tracking error plays a leading role. When the state error is large, the LADRC weight with stronger anti-interference ability is greater than NLADRC. When the state error is small, the NLADRC weight with higher selection efficiency is greater than LADRC. The rule base and corresponding results of LADRC and NLADRC output weights are shown in Tables 1 and 2, respectively, where \(\underline{y}_{L}^{i}\), \(\underline{y}_{N}^{i}\), and \(\overline{y}_{L}^{i}\), \(\overline{y}_{N}^{i}(i=1,2,\cdots,9)\) represent the lower bound and upper bound of the weight rule output for LADRC and NLADRC, respectively. The center of gravity of a type of fuzzy set composed of these single values is found by replacing each regular consequent with a single value located at its center of gravity with a central reducer. The equations can describe as follows: