###### Abstract  
This study introduces an enhanced Adaptive Fuzzy Sliding-Mode Control (AFSMC) approach based on the fuzzy logic systems (FLSs) to achieve trajectory tracking of multiple-input and multiple-output (MIMO) fractional-order nonlinear systems in the presence of uncertain nonlinear terms and disturbances. An integral SMC approach is proposed for achieving state trajectory tracking control. However, uncertainties in real systems are complex and diverse, not only uncertain bounded disturbances but unknown nonlinear functions. Therefore, in this paper, the FLSs are used not only to approximate unknown functions but also to improve the switching function of the SMC. The stability of the system with designed input control laws is demonstrated through the fractional-order Lyapunov function stability criterion. Subsequently, the simulation results are displayed and serve to validate the efficacy and resilience  
demonstrated in [12], the UAV model displayed a complex state equation that imposed very high requirements on the robustness and real-time performance of the control algorithm because of the underactuated structure, nonlinear SE(3) dynamics, strong coupling of control allocation, and sensor noise in the real system. The authors of [13] presented a fractional-order strict feedback nonlinear system with fuzzy dead zones. Researchers have proposed a number of fractional intelligent-control methods, including the typical SMC, a type of control method for nonlinear systems, to solve this type of nonlinear system with nonlinear factors and uncertainty factors. Compared with the usual backstepping method, the SMC is more widely used in the lower triangle nonlinear system, and it can solve the exponential explosion in the backstepping control. The traditional SMC is a relatively mature development and is applied in various control technology schemes, such as synchronization control and tracking control. The initial applications of the SMC in [14; 15; 16; 17; 18; 19; 20] showed a strong robustness to interferences and system uncertainties. [PERSON] found a fractional-order chaotic system based on the PMSM and proposed the PI-SMC method applied in [14], and the method was improved by [PERSON] and his partners in [15]. A fractional-order exponential switching function was proposed to enhance the dynamic behavior of the SMC in [17]. The SMC was applied in a single flexible link manipulator and had an advantage compared with the classical PD controller in [20]. Uncertain disturbances or unknown parameters are typically designed as bounded values when creating an adaptive sliding-mode controller. An adaptive control law is then created for the bounded values. However, there are a variety of disturbances and uncertainty factors in the systems application process. Especially, uncertain nonlinear function terms are present in the system as a result of uncertain disturbances that are frequently present in nonlinear functions due to state measurement errors or device losses. In order to solve this problem, the FISs are introduced to deal with unknown functions. In [21], an AFSMC was designed to control MIMO uncertain subsystem models linearized by the full state input-output feedback linearization control, and the controller can effectively suppress the uncertain bounded external interference. Back in 2013 [22], an integer-order AFSMC approach was proposed that combined the excellencies of the SMC and FLSs, and adaptive fuzzy control was used to calculate the switching function of PD-SMC. As FLSs have had many applications in integer-order systems, we investigate their suitability for fractional-order nonlinear systems. The AFSMC has proved that it can not only handle single or multiple uncertainties but also can be used in the case of fractional or integer-order systems, e.g., in [23]. Sliding-mode control, fuzzy control, network systems, and even infinity control were all combined by the author and compared to sliding-mode control [24].