## 1 Introduction  
With the advent of the Internet, multi-rotor unmanned aerial vehicles (UAVs) have rapidly developed not only in the consumption field but also in the sectors of logistics and transportation, warehouse inspection, and agricultural spraying [1]. The use of multi-rotor UAVs reduces the human need to enter a hazardous operating zone, while increasing labor productivity. However, with the increase in the number of the airborne equipment and tasks to be performed, multi-rotor UAVs are no longer a simple nonlinear systems [2]. They have begun to evolve toward a more complex system [3, 4, 5, 6]. For an underactuated system with nonlinear coefficients, a sliding-mode controller is usually used for position and attitude tracking [7, 8, 9]. Alternatively, adaptive technology is combined with a sliding-mode controller to achieve stability in finite time [10]. However, compared with the methods that improve the system robustness, the adaptive self-organizing fuzzy neural network also produces better control performance for quadrotor attitude tracking with uncertainty in the dynamic parameters and environmental disturbances [11]. For disturbance-sensitive aircraft flight control systems, when active disturbance rejection control (ADRC) is applied to a nonlinear and strongly coupled system, it can also achieve system stability under external disturbances [12]. In practice, instability of the control system due to the faults in the actuator and sensors is a common occurrence in UAVs [13]. Along with the development of fault-tolerant control (FTC) theory [14; 15; 16; 17], several researchers have attempted to use the robust adaptive approach to solve the FTC problem of a nonlinear system with actuator faults [18; 19]. In recent years, a growing number of studies have combined intelligent control with fault diagnosis while investigating the FTC method for UAVs [20; 21]. In the meantime, FTC algorithms addressing actuator faults have been constantly emerging [22; 23; 24]. [PERSON] et al. proposed an FTC scheme that combines continuous and discrete frameworks to address multiple actuator faults [25]. To compensate for the unknown disturbance faults in an aircraft attitude control system, a fault observer was designed based on the radial basis function (RBF) neural network [16]. However, if uncertain modeling terms and unknown parameters of the fault terms exist in a system, it is necessary to improve the control scheme of the system. In this pursuit, a fuzzy neural network was integrated with a multivariable sliding-model controller to design a fault-tolerant flight controller that was stable in finite time [26]. The described method effectively identified the uncertain modeling terms and the unknown parameters of the fault terms in the system. For a multi-input, multi-output system featured by nonlinearity, strong coupling, and uncertainty, [PERSON] et al. proposed an FTC scheme by integrating the ADRC and RBF [27]. This control scheme was able to handle the faults of an actuator, sensors, and other components. However, the studies on the flight safety control theories of plant-protection UAVs are still in the preliminary stage [28]. Plant-protection UAVs usually work under wind disturbance. Therefore, the disturbance features of lateral wind should be incorporated into the design of the aircraft flight controller [29]. The influence of wind disturbance was considered in the fault system of an actuator [30]. The fault-tolerant stability control of the system was realized using an ADRC controller. However, few studies have been conducted to date on multiple time-varying actuator faults. The use of the RBF neural network as an extended state observer (ESO) was further extended to the control system of a plant-protection UAV with actuator faults [31]. An RBF neural network for predicting the time series of aircraft state parameters was also reported [32]. This RBF neural network was combined with the ADRC controller designed with optimized parameters to construct a fault-tolerant aircraft control (FTAC) scheme.