## 1 Introduction  
Near-space morphing vehicles (NMV) are a kind of special vehicle that can change their shape and structure. NMVs can change the aerodynamic configuration according to the flight environment, flight conditions, and flight mission so as to optimize the control characteristics, improve flight efficiency, and enhance maneuverability [1; 2]. However, NMVs possess the characteristics of strong nonlinearity and strong coupling and suffer from model uncertainty and unknown disturbance as well as changes to the airframe structure and aerodynamic parameters caused by the winglet stretch out or draw back, which bring great challenges to the design of the flight control system [3; 4; 5]. The active disturbance rejection control (ADRC) method estimates and compensates the total unknown disturbances in real time via an expanded state observer. At the same time, it is modified by the nonlinear state error feedback control law, and the process errors are used to eliminate the system errors to improve control performance [6; 7; 8]. The ADRC method does not rely on the system model and has great advantages in the control of uncertain systems [9; 10]. [PERSON] et al. [11] designed an ADRC for an angle of attack autopilot and solved the problems of the requirements of angle of attack and angle of sideslip for hypersonic vehicles. In [12], the authors designed a robust output feedback autopilot for airbreathing hypersonic vehicles based on ADRC. In [13], the authors proposed a novel ADRC method for hydraulic systems with full-state constraints and input saturation. [PERSON] et al. [14] proposed a double degree-of-freedom (DOF) control strategy based on the extended Kalman filter (EKF) and adaptive active disturbance rejection control, which resulted in fast dynamic response and strong anti-interference capability. Since fuzzy control was first proposed, it has been widely used in solving the control problems for nonlinear systems with model uncertainty. [PERSON] et al. [15] proposed a fuzzy optimal tracking control method and designed main controllers for the velocity and altitude subsystems of hypersonic flight vehicles. [PERSON] et al. [16] proposed low-complexity design strategies for waverider aircraft via fuzzy neural approximation. Compared with the type-1 fuzzy logic system, the type-2 fuzzy logic system has more freedom for the definition of fuzzy sets and can better solve the problems of uncertainty, incompleteness, and data noise in information. In [17], the authors designed a robust controller based on the interval type-2 fuzzy logic system for a six degrees of freedom (6 DOF) coaxial trirotor helicopter in order to eliminate the chattering phenomenon and guarantee the stability and robustness of the system. In [18], the authors compared type-1 and type-2 fuzzy neural networks (T2 FNNs) for the trajectory tracking problem of quadrotor VTOL aircraft in terms of their tracking accuracy and control efforts. The results showed that T2 FNN structures have better noise reduction properties compared to their type-1 counterparts in the presence of unmodeled noise and disturbances. [PERSON] et al. [19] proposed a multiple-step fault estimation algorithm for hypersonic flight vehicles. The interval type-2 Takagi-Sugeno fuzzy model was used to approximate the nonlinear dynamic system and handle parameter uncertainties. [PERSON] et al. [20] designed an adaptive sliding mode control method for hypersonic flight vehicles based on the type-2 fuzzy logic system. [PERSON] et al. [21] proposed a robust adaptive tracking control method for hypersonic vehicles based on the interval type-2 fuzzy logic system. In [22], the authors proposed an improved adaptive fault-tolerant control strategy for the interval type-2 fuzzy logic system in hypersonic flight vehicles. In [23], the authors proposed a novel fixed-time adaptive control method for air-breathing hypersonic vehicles via the type-2 fuzzy logic system. In terms of switching control for hypersonic vehicles, [PERSON] et al.